Olfactory signature and odorant mixture having the same

ABSTRACT

An odorant mixture is disclosed. The odorant mixture comprises N odorant components wherein N equals at least 20. Each odorant component is characterized by a multidimensional vector of attributes. A z score of an average of characteristic distances between vectors corresponding to odorant components in the mixture and vectors corresponding to odorant components in a group of M odorant components but not in the mixture is less than 2.

RELATED APPLICATIONS

This application is a division of U.S. patent application Ser. No. 14/343,064 filed on Mar. 6, 2014, which is a National Phase of PCT Patent Application No. PCT/IB2012/054621 having International Filing Date of Sep. 6, 2012, which claims the benefit of priority under 35 USC § 119(e) of U.S. Provisional Patent Application No. 61/531,689 filed on Sep. 7, 2011. The contents of the above applications are all incorporated by reference as if fully set forth herein in their entirety.

FIELD AND BACKGROUND OF THE INVENTION

The present invention, in some embodiments thereof, relates to an odorant mixture and, more particularly, but not exclusively, to an odorant mixture having an olfactory signature, and method of characterizing an odorant mixture using its olfactory signature.

Several studies have linked human perception of monomolecular odorants to the odorant's physical structure and its specific receptors [Khan et al. (2007) Predicting odor pleasantness from odorant structure: pleasantness as a reflection of the physical world, The Journal of Neuroscience 27(37):10015; Kermen et al. (2011) Molecular complexity determines the number of olfactory notes and the pleasantness of smells, Sci Rep 1:206; Keller et al., (2007) Genetic variation in a human odorant receptor alters odour perception, Nature 449(7161):468-472; and Menashe et al. (2007) Genetic elucidation of human hyperosmia to isovaleric acid. PLoS Biol 5(11):e284].

Odors are complex mixtures of chemical species, and so contain many constituent molecules. The biological olfactory system is a remarkable sensor having many olfactory cells or odorant receptors, but not very many different types of olfactory cells. The characterization of a scent or odor is typically through the combined response of many of the receptors.

Haddad et al. [A metric for odorant comparison, Nature Methods-5, 425-429 (2008)] describes a multidimensional physicochemical metric that took into account many molecular descriptors. Molecular descriptors are mathematical values that describe the structure or shape of a molecule. In Haddad et al., each odorant molecule is represented as a vector containing its descriptor values, and the Euclidean distance between any two odorants is used as a multidimensional metric.

Additional background art includes Saito et al., “Odor Coding by a Mammalian Receptor Repertoire”, Sci. Signal. 2, ra9 (2009).

SUMMARY OF THE INVENTION

According to an aspect of some embodiments of the present invention there is provided an odorant mixture, comprising N odorant components wherein N equals at least 20, each odorant component being characterized by a multidimensional vector of attributes, such that the mixture is characterized by N respective multidimensional vectors, wherein a z score of an average of characteristic distances between vectors corresponding to odorant components in the mixture and vectors corresponding to odorant components in a group of M odorant components but not in the mixture is less than 2.

According to some embodiments of the invention at least a portion of the N odorant components is selected from the group of M odorant components.

According to some embodiments of the invention the N odorant components have similar odor intensity.

According to some embodiments of the invention each characteristic distance is defined as a minimum distance between a vector corresponding to odorant components in the mixture and a vector corresponding to an odorant component in the group of M odorant components but not in the mixture.

According to some embodiments of the invention each of the N odorant components is in a gaseous state.

According to some embodiments of the invention the z-score is with respect to a synthetic database which comprises a plurality of entries, each corresponding to a database odorant mixture defined as being producible from a plurality of odorant components selected from the group of M odorant components.

According to an aspect of some embodiments of the present invention there is provided a method of at least partially masking a target odor in an environment. The method comprises spreading an odorant mixture as described herein in the environment, thereby providing a combined mixture having the odorant mixture and odorant components of the target odor.

According to some embodiments of the invention a z score of an average of characteristic distances between vectors corresponding to odorant components in the combined mixture and vectors corresponding to odorant components in the group of M odorant components but not in the combined mixture is less than 1.

According to an aspect of some embodiments of the present invention there is provided a method of determining an olfactory signature of an odorant mixture having N odorant components, the method being executable by a data processor. The method comprises: accessing a database having a group of M odorant components; for each odorant component, obtaining a multidimensional vector of attributes, thereby providing a plurality of multidimensional vectors; calculating a z score of an average of characteristic distances between vectors corresponding to odorant components in the mixture and vectors corresponding to odorant components in the group of M odorant components but not in the mixture; and generating an output comprising the z score, the z score defining the olfactory signature of the odorant mixture.

According to some embodiments of the invention each characteristic distance is defined as a minimum distance between a vector corresponding to odorant components in the mixture and a vector corresponding to an odorant component in the group of M odorant components but not in the mixture.

According to some embodiments of the invention the z-score is calculated with respect to a synthetic database which comprises a plurality of entries, each corresponding to a database odorant mixture defined as being producible from a plurality of odorant components selected from the group of M odorant components.

According to some embodiments of the invention the method comprises generating the synthetic database.

According to some embodiments of the invention M is at least equal to N.

According to some embodiments of the invention each of the multidimensional vectors has at least 50 dimensions or at least 100 dimensions or at least 1000 dimensions.

According to some embodiments of the invention N equals at least 30 or at least 40 or at least 50.

According to some embodiments of the invention M equals at least 100 or at least 1000.

According to some embodiments of the invention the group of M odorant components is selected from the odorant components listed in Table A.1 or Table A.2 of Annex 1.

According to some embodiments of the invention the group of M odorant components is the group listed in Table A.1 of Annex 1.

According to some embodiments of the invention the group of M odorant components is the group listed in Table A.2 of Annex 1.

Unless otherwise defined, all technical and/or scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the invention pertains. Although methods and materials similar or equivalent to those described herein can be used in the practice or testing of embodiments of the invention, exemplary methods and/or materials are described below. In case of conflict, the patent specification, including definitions, will control. In addition, the materials, methods, and examples are illustrative only and are not intended to be necessarily limiting.

Implementation of the method and/or system of embodiments of the invention can involve performing or completing selected tasks manually, automatically, or a combination thereof. Moreover, according to actual instrumentation and equipment of embodiments of the method and/or system of the invention, several selected tasks could be implemented by hardware, by software or by firmware or by a combination thereof using an operating system.

For example, hardware for performing selected tasks according to embodiments of the invention could be implemented as a chip or a circuit. As software, selected tasks according to embodiments of the invention could be implemented as a plurality of software instructions being executed by a computer using any suitable operating system. In an exemplary embodiment of the invention, one or more tasks according to exemplary embodiments of method and/or system as described herein are performed by a data processor, such as a computing platform for executing a plurality of instructions. Optionally, the data processor includes a volatile memory for storing instructions and/or data and/or a non-volatile storage, for example, a magnetic hard-disk and/or removable media, for storing instructions and/or data. Optionally, a network connection is provided as well. A display and/or a user input device such as a keyboard or mouse are optionally provided as well.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

Some embodiments of the invention are herein described, by way of example only, with reference to the accompanying drawings.

With specific reference now to the drawings in detail, it is stressed that the particulars shown are by way of example and for purposes of illustrative discussion of embodiments of the invention. In this regard, the description taken with the drawings makes apparent to those skilled in the art how embodiments of the invention may be practiced.

In the drawings:

FIGS. 1A-1B are graphs illustrating odorants plotted in stimulus space. A. Perceptual space: 144 odorants commonly used in olfaction-research projected onto a two-dimensional space made of PC1 (30.8% of the variance) and PC2 (12% of the variance) of perception; B. Physicochemical space: 1492 odorants commonly modeled in olfaction-research projected onto a two-dimensional space made of PC1 (33.4% of the variance) and PC2 (10% of the variance) of structure. The 86 odorants used are plotted in red. Considerations of human safety prevented the inclusion of odorants that were at the extremes of physicochemical space, as these are often toxic.

FIGS. 2A-2C are graphs illustrating that increasing the number of non-overlapping spanned components in two mixtures renders them more similar and less discernible. A. The average rated similarity for mixture pairs differing in number of components, where mixtures were selected from perceptual space (square) or physicochemical space (diamond). Component number is expressed as the square root of the product of the two mixture sizes B. Discrimination accuracy between a 35-component mixture and nonoverlapping mixtures of various sizes. C. Confidence level ratings. Error bars are standard error.

FIGS. 3A-3B are graphs illustrating that mixtures made of many equal-intensity spanned components are identified as olfactory white. The average probability of identifying a novel odorant mixture as white (“Laurax”) as a function of the number of components in the mixture either in a 4-alternative forcedchoice identification test (A), or a 5-alternative forced-choice identification test (B). Each dot represents the average rating for four versions of a given number of components. Error bars are standard error. Inset: the average probability of identifying a novel odorant mixture as white (Laurax), for mixtures of less of 20 components, or mixtures of equal or more than 20 components.

FIGS. 4A-4B are graphs illustrating that mixtures made of many equal-intensity spanned components match the perceptual memory of olfactory white. A. Delayed match-to-sample between a learned Laurax and novel non-overlapping mixtures of various sizes. Each dot represents the average rating for three versions of a given number of components. Error bars are standard error. Inset: the average probability of discrimination for mixtures of less than 20 components or mixtures of 20 or more components. B. Delayed match-to-sample between a learned Laurax and novel non-overlapping 25-component mixtures that were either spanned in space, clustered in space, not equated for intensity, or the very same Laurax they learned (100% accuracy). Error bars are standard error between all subjects and versions.

FIG. 5 is a graphical characterization of the smell of olfactory white. Ratings given to 12 monomolecular odorants (diamond), four 10-component mixtures that optimally spanned space (square), and four 40-component mixtures that optimally spanned space (triangle), along the two key axes of human olfactory perception, (A) one from very unpleasant to very pleasant, and (B) one from highly poisonous to highly edible. Error bars are standard error between subjects.

FIGS. 6A-6H are graphs illustrating the rated similarity as a function of number of components in a mixture (mixture components spread in physicochemical space). A-H, The correlation of the normalized average rated similarities between 1, 4, 10, 15, 20, 30 and 40/43-component mixtures with a non-overlapping ‘target mixture’ consisting of A, 43 components, B, 40 components, C, 30 components, D, 20 components, E, 15 components, F, 10 components, G, 4 components, and H, 1 component. Each point represents the average rated similarity of one pairwise comparison. These data reflect the comparisons from odorant sets 3 (triangle) and 4 (solid circle) of Table 1 in which the mixtures were selected according to their components' physicochemical spread. Error bars are standard error across subject ratings.

FIGS. 7A-7F are graphs illustrating the rated similarity as a function of number of components in a mixture (mixture components spread in perceptual space). A-F, The correlation of the normalized average rated similarities between 1, 4, 10, 20, 30 and 40-component mixtures with a non-overlapping ‘target mixture’ consisting of A, 40 components, B, 30 components, C, 20 components, D, 10 components, E, 4 components, F, 1 component. Each point represents the average rated similarity of one pairwise comparison. These data reflect the comparisons from odorant sets 1 (triangle) and 2 (solid circle) of Table 1, in which the mixtures were selected according to their components' perceptual spread. Error bars are standard error across subject ratings.

FIGS. 8A-8B are graphs representing the identification of laurax after 6 months. The labels applied to (1) Five versions of Laurax, (2) β-phenylethyl alcohol, (3) Geraniol, (4) Citronellol, (5) Nerol, and (6) the rose mixture. The averaged response (in percentage) of 10 subjects in a 7-alternative (A) and 4-alternative (B) forcedchoice identification task.

FIG. 9 is a graph illustrating natural odors form clusters in olfactory space. T(G) of Laurax (average of 40 and 60 component mixtures) (square), 63 components of rose (solid circle), 35 of coffee (diamond) (5), 37 components of an apple (triangle) (6). The present inventors ran 10,000 iterations to generate random mixtures of the appropriate component size mixtures (i.e. 60, 40, 63, 36). The histogram represents all 40,000 T(G) values (z-scored). Smaller value of T(G) represents a better spanning of the physicochemical space. As shown, whereas natural odors form an extreme cluster in this physicochemical space, Laurax reflects an average spread in this space.

DESCRIPTION OF SPECIFIC EMBODIMENTS OF THE INVENTION

The present invention, in some embodiments thereof, relates to an odorant mixture and, more particularly, but not exclusively, to an odorant mixture having an olfactory signature, and method of characterizing an odorant mixture using its olfactory signature.

Before explaining at least one embodiment of the invention in detail, it is to be understood that the invention is not necessarily limited in its application to the details of construction and the arrangement of the components and/or methods set forth in the following description and/or illustrated in the drawings and/or the Examples. The invention is capable of other embodiments or of being practiced or carried out in various ways.

The rules underlying perception of complex olfactory mixtures, remain largely unknown. On one hand, humans are very poor at identifying the components of a mixture, even when they are able to identify the components alone. In turn, humans remain exquisitely capable of discriminating one mixture from another, and mixtures containing hundreds of different volatile molecules are associated with unique olfactory percepts such as wine, roasted coffee, or rose.

While conceiving the present invention it has been hypothesized and while reducing the present invention to practice it has been realized that an odorant mixture having a plurality of odorant components which are distributed sufficiently uniformed over an olfactory space, has a generally common olfactory percept.

Thus, according to some embodiments of the present invention there is provided an odorant mixture. Optionally and preferably the odorant mixture is in gaseous state. Odorant mixture in liquid or solid states is not excluded from the scope of the present invention. In various exemplary embodiments of the invention the odorant mixture comprises N odorant components where N equals at least 10 or at least 20 or at least 30 or at least 40 or at least 50 or at least 60.

As used herein, an “odorant component” is a monomolecular substance which can be sensed by the olfactory receptors and is perceived as having a smell in humans.

Each of the odorant components is optionally and preferably characterized by a multidimensional vector of attributes. The number of attributes in the vector (namely, the dimensionality of the vector) is preferably large, e.g., at least 50 or at least 60 or at least 70 or at least 80 or at least 90 or at least 100 or at least 110 or at least 120 or at least 130 or at least 140 or at least 1000 or at least 1100 or at least 1200 or at least 1300 or at least 1400 or at least 2000 or at least 3000 or at least 4000.

While the vectors typically have large dimensionality, the dimensionality of one or more of the vectors, e.g., all the vectors, can, optionally, be reduced using a dimensionality reduction procedure. A representative example of a dimensionality reduction procedure includes, without limitation, a principal component analysis (PCA).

PCA is a known data analysis procedure. In PCA, a correlation matrix is constructed from the vectors and a set of eigenvalues of the correlation matrix can be calculated. A subset of the set of eigenvalues can then be selected. The subset is optionally and preferably of the largest eigenvalues of the correlation matrix, which typically corresponds to the most “important” dimensions of the vectors. The correlation matrix is constructed based on correlations which are calculated between different components of each data entry, and are averaged over all the data entries. The correlation matrix can be of any type, including, without limitation, an autocorrelation matrix and a covariance matrix. The correlation matrix provides a natural basis to span the space, which basis is specified by the eigenvectors of the matrix which are known as the principal components of the dataset.

The attributes of a particular odorant component can include perceptual descriptors and/or physicochemical descriptors. Perceptual descriptors can be obtained from the literature, see, for example, Dravnieks, A. Odor quality: semantically generated multi-dimensional profiles are stable. Science 218, 799-801 (1982); and Dravnieks, A. Atlas of odor character profiles (ASTM Press, P A, 1985), the contents of which are hereby incorporated by reference. A representative list of 146 perceptual descriptors descriptor names is provided in the middle column of Table 4 in the Examples section that follows.

Physicochemical descriptors can include, for example, molecular descriptors which can be obtained using a data processor supplemented with a dedicated algorithm. Alternatively, dedicated circuitry can be used. Software suitable for obtaining a vector of physicochemical descriptors according to some embodiments of the present invention includes, without limitation, the Dragon software marketed by Talete, Milan, Italy. For example, version 6 of this software can provide 4885 molecular descriptors. In experiments performed by the present inventors, the Dragon software was used to extract 1438 physicochemical descriptors for each odorant component.

Thus, the odorant mixture of the present embodiments is characterized by a collection G of N respective multidimensional vectors g (one multidimensional vector for each odorant component) and can therefore be described in terms of a multidimensional space containing this collection. This multidimensional space is referred to herein as an “olfactory space”. It will be appreciated that the present embodiments contemplate more than one olfactory space. For example, when the individual odorant components are described using the 146 perceptual descriptors listed in Table 4, the olfactory space has 146 dimensions, and when the individual odorant components are described using the 1438 physicochemical descriptors, the olfactory space has 1438 dimensions.

The respective vectors of the odorant mixture are optionally and preferably distributed substantially uniformly over the olfactory space. The uniformity of the vectors can be quantified using a metric defined over the olfactory space.

Hence, in some embodiments of the present invention a database having a group K of M odorant components is accessed, and a multidimensional vector of attributes is obtained for each of the M odorant components as further detailed hereinabove. Thus, group K is characterized using a collection of M vectors, referred to herein as collection X.

Optionally and preferably, M is at least equal to N. For the purpose of the quantification, it is advantageous to employ a group K with large number of odorant components spanning over large portion of the olfactory space. Thus, in some embodiments of the present invention M equals at least 100 or at least 1000. Representative examples of groups of odorant components are provided in Tables A.1 and A.2 of Annex 1, below. The group K of the present embodiments can include at least a portion, more preferably, all the odorant components is the group listed in Table A.1 or Table A.2 of Annex 1.

For each vector x which is in the collection X but not in collection G (formally, xϵX, x∉G), a distance between x and G is calculated. Since G includes many vectors, the distance between x and G can be defined in more than one way. Typically, but not necessarily, the distance between x and G can is defined as the minimum distance that can be obtained between x and any of the vectors in G. Formally, this distance can be written as D(x,G)=min_(gϵG)d(x,g), where g is a vector in G and d(x,g) is the distance between x and g.

The distance d can be according to any metric. For example, an Euclidian distance can be calculated. Also contemplated are other metrics, including, without limitation, Minkowski metric, in which case d is a Minkowski distance.

The distance is preferably can be calculated using all the components of the vectors. In some embodiments, the distance is calculated based on a reduced dimensionality. For example, a principal component analysis can be performed, and the distance can be calculated using the one or more of the components extracted by the PCA. Typically, but not necessarily, the first p components are used, where p is an integer satisfying p≤10 or p≤9 or p≤8 or p≤7 or p≤6 or p≤5 or p≤4 or p≤3 or p≤2.

The distance D(x,G) is optionally and preferably calculated for at least some more preferably all the vectors x. The uniformity of the vectors in the collection G over the olfactory space can then be related to the average of D(x,G) over x. More specifically, denoting this average by T(G), smaller values of T(G) correspond to a more uniform distribution of the vectors in the collection G over the olfactory space, wherein higher values of T(G) correspond to large regions in odorspace where vectors in the collection G are not present.

In some embodiments of the present invention the z score of T(G) is less than 2 or less than 1.5 or less than 1 or less than 0.5 or less than 0 or less than −0.5.

A z-score of T(G) can be calculated, for example, in relation to a database of other odorant mixtures, each being characterized by an average distance T calculated over the collection X (corresponding to group K) as described above. Specifically, let μ_(T) and σ_(T) be, respectively, the mean and standard deviation of T as calculated for the entire database. The z-score of T(G), Z_(T(G)), can be calculated based on μ_(T) and σ_(T), as known in the art, e.g., using the relation Z_(T(G))=(T(G)−μ_(T))/σ_(T).

The database is optionally and preferably a synthetic database stored on a computer readable medium. A synthetic database can include, for example, a plurality of entries, each corresponding to one database odorant mixture defined as being producible from a plurality of odorant components selected, optionally and preferably in a random manner, from the group K. Thus, each entry in the database is also characterized by a collection of vectors, one vector for each odorant component of the respective database odorant mixture. The number of vectors characterizing each database entry is referred to herein as the size of the respective entry.

Preferably, but not necessarily, the size of each entry in the database is approximately the same as the number of odorant components in the collection G. Use of database having entries with a variety of sizes is not excluded from the scope of the present invention. Typically, the number of entries in the database is at least 100 or at least 200 or at least 400 or at least 800 or at least 1600 or at least 3200 or at least 6400, e.g., about 10,000 entries or more.

The present embodiments thus provide an efficient technique for calculating, within a multidimensional space, an average distance T using the multidimensional vectors corresponding to the odorant components constituted in the odorant mixture and the multidimensional vectors corresponding to odorant components in group K but not in the odorant mixture. This average distance is characteristic to the olfactory percept and can therefore be used as, or related to, the olfactory signature of the mixture. Thus, in some embodiments of the present embodiments T(G) is defined as the olfactory signature of the mixture characterized by the collection G, and in some embodiments of the present embodiments the z-score of T(G) is defined as the olfactory signature of the mixture characterized by the collection G. Other quantities based on T(G), such as, but not limited to, the logarithm of T(G) or some normalized representation of T(G), are not excluded from the scope of the present invention for the purpose of defining the olfactory signature.

The odorant mixture of the present embodiments is thus characterized by large span and high uniformity over the olfactory space. In experiments performed by the present inventors it was found that such odorant mixture has a generally common olfactory percept. Borrowing the terminologies from color vision wherein many different wavelengths eventually generate color percept of white, and tonal audition wherein many different frequencies eventually generate white noise, the present inventors coined the term “olfactory white” for an odorant mixture having sufficiently large span and sufficiently high uniformity over the olfactory space.

The T distribution property of the present embodiments can be identified in a mixture by comparing it to a plurality of database mixtures, as shown, for example, in FIG. 9 of the Examples section that follows. An olfactory white mixture can be defined as having a T value which is from about 0.5 to about 5 standard deviations off the center of the distribution of T values for randomly selected mixtures.

Representative examples of odorant mixtures with sufficiently large span and sufficiently high uniformity over the olfactory space are provided in Tables 7A-C of the Examples section that follows.

In various exemplary embodiments of the invention the odorant components in the odorant mixture have similar odor intensity. The odor intensity can be determined by conducting olfaction tests for a group of subjects and analyzing the odor strength as perceived by the subjects. A representative example of a procedure for determining the perceived odor intensity is described in the Example section that follows.

While it is advantageous to equate the odor intensity in the odorant mixture in order to provide a generally common olfactory percept, it is to be understood that this need not necessarily be the case, since it was found by the present inventors that odorant mixture with sufficiently large span and sufficiently high uniformity over the olfactory space can have a common olfactory percept, even when not all the odorant components are present at the same odor intensity.

An odorant mixture according to some embodiments of the present invention can be used for at least partially masking a target odor in an environment. This can be done by spreading a sufficient amount of odorant mixture in the environment, to provide a combined mixture having the odorant mixture and the odorant components of the target odor. The present inventors found that when the odorant mixture has sufficiently large span and sufficiently high uniformity over the olfactory space it can provide a common olfactory percept even when combined with the odorant components of the target odor.

In some embodiments of the present invention the odorant mixture is selected such that the combined mixture has a sufficiently large span and a sufficiently high uniformity over the olfactory space. For example, the odorant mixture can be selected such that the z score of T(G*), where G* denotes the collection of vectors corresponding to the odorant components in the odorant mixture and the vectors corresponding to the odorant components in the target odor, is less than 1 or less than 0.5 or less than 0 or less than −0.5. Thus, T(G*) can be used for defining the olfactory signature of the combined odorant mixture, as further detailed hereinabove, mutatis mutandis with respect to T(G).

According to some embodiments of the present invention there is provided a method of determining an olfactory signature of an odorant mixture. Selected operations of the method described below can be executed by a data processor, such as a general purpose computer or a dedicated circuitry.

The method of the present embodiments can be embodied in many forms. For example, it can be embodied in on a tangible medium such as a computer for performing the method steps. It can be embodied on a computer readable medium, comprising computer readable instructions for carrying out the method steps. In can also be embodied in electronic device having digital computer capabilities arranged to run the computer program on the tangible medium or execute the instruction on a computer readable medium.

Computer programs implementing the method of this invention can commonly be distributed to users on a distribution medium such as, but not limited to, a floppy disk, a CD-ROM, a flash memory device. From the distribution medium, the computer programs can be copied to a hard disk or a similar intermediate storage medium. Alternatively, the computer programs can be downloaded to the hard disk or intermediate storage medium) from a server, e.g., via the internet. The computer programs can be run by loading the computer instructions either from their distribution medium or their intermediate storage medium into the execution memory of the computer, configuring the computer to act in accordance with the method of this invention. All these operations are well-known to those skilled in the art of computer systems.

The method of the present embodiments is typically useful for determining an olfactory signature of an odorant mixture having N odorant components. In some embodiments, the method receives the odorant components as input and in some embodiments of the present invention the method extracts the odorant components from the mixture itself. The extraction can be performed using any chemical analysis procedure known in the art. A representative and non-limiting example of a process suitable for extracting the odorant components from the mixture according to some embodiments of the present invention, include, without limitation, Gas Chromatography—Mass Spectrometry, which is described in many textbooks (see, for example, “GC/MS: A Practical User's Guide, 2nd Edition”, 2011, by Marvin McMaster, ISBN: 978-1-1182-1005-5; “Handbook of GC/MS: Fundamentals and Applications, 2nd, Completely Revised and Updated Edition,” 2008, by Hans-Joachim Hubschmann, ISBN: 978-3-527-31427-0; and also the online Wikipedia article www(dot)en(dot)Wikipedia(dot)org/wiki/Gas_chromatography-mas s spectrometry).

In various exemplary embodiments of the invention the method accesses a database having a group K of M odorant components. For each odorant component of the mixture and each odorant component of K, a multidimensional vector of attributes is obtained, to provide a plurality of multidimensional vectors. The multidimensional vectors corresponding to the odorant components of K can be received by the method as input. For example, the accessed database can include the multidimensional vector corresponding to each odorant component entry in the database. The multidimensional vectors corresponding to the odorant components of the odorant mixture can be calculated as further detailed hereinabove or they can be received as input from a suitable input system, such as a data processor configured to provide a multidimensional vector for a given odorant component.

The obtained multidimensional vectors are then used for calculating an average of characteristic distances between vectors corresponding to the odorant components in the mixture and vectors corresponding to the odorant components in the group K, as further detailed hereinabove. An output comprising the average is then generated. The output can include the average itself, or some related quantity such as, but not limited to, the z-score of the average, a logarithm of the average, a normalized representation of the average, etc.

When a z-score of the average is calculated, the z-score is optionally and preferably in relation to a database of other odorant mixtures. The database is optionally and preferably a synthetic database as further detailed hereinabove. The database can be stored on a computer readable medium. Alternatively, the method according to some embodiments of the present invention can generate the database. This can be done, for example, by randomly selecting sub-groups of odorant components from the group K, and defining each selected sub-group as a database odorant mixture corresponding to one database entry. The number of components in each sub-group can be the same as the number of odorant components in the collection of vectors describing the odorant mixture under analysis. Alternatively, two or more sub-groups may have different numbers of odorant components thereamongst, thereby generating a database having entries with a variety of sizes.

As stated, the average or related quantity can be used for defining the olfactory signature of the odorant mixture. Thus, the generated output according to some embodiments of the present invention includes the olfactory signature of the odorant mixture. The output can be generated by displaying the average or related quantity on a display device, printing the average or related quantity using a printer, transmitting the average or related quantity to a computer readable medium (e.g., a memory medium or a storage medium), and the like.

As used herein the term “about” or “approximately” refers to ±10%.

The word “exemplary” is used herein to mean “serving as an example, instance or illustration.” Any embodiment described as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments and/or to exclude the incorporation of features from other embodiments.

The word “optionally” is used herein to mean “is provided in some embodiments and not provided in other embodiments.” Any particular embodiment of the invention may include a plurality of “optional” features unless such features conflict.

The terms “comprises”, “comprising”, “includes”, “including”, “having” and their conjugates mean “including but not limited to”.

The term “consisting of” means “including and limited to”.

The term “consisting essentially of” means that the composition, method or structure may include additional ingredients, steps and/or parts, but only if the additional ingredients, steps and/or parts do not materially alter the basic and novel characteristics of the claimed composition, method or structure.

As used herein, the singular form “a”, “an” and “the” include plural references unless the context clearly dictates otherwise. For example, the term “a compound” or “at least one compound” may include a plurality of compounds, including mixtures thereof.

Throughout this application, various embodiments of this invention may be presented in a range format. It should be understood that the description in range format is merely for convenience and brevity and should not be construed as an inflexible limitation on the scope of the invention. Accordingly, the description of a range should be considered to have specifically disclosed all the possible subranges as well as individual numerical values within that range. For example, description of a range such as from 1 to 6 should be considered to have specifically disclosed subranges such as from 1 to 3, from 1 to 4, from 1 to 5, from 2 to 4, from 2 to 6, from 3 to 6 etc., as well as individual numbers within that range, for example, 1, 2, 3, 4, 5, and 6. This applies regardless of the breadth of the range.

Whenever a numerical range is indicated herein, it is meant to include any cited numeral (fractional or integral) within the indicated range. The phrases “ranging/ranges between” a first indicate number and a second indicate number and “ranging/ranges from” a first indicate number “to” a second indicate number are used herein interchangeably and are meant to include the first and second indicated numbers and all the fractional and integral numerals therebetween.

It is appreciated that certain features of the invention, which are, for clarity, described in the context of separate embodiments, may also be provided in combination in a single embodiment. Conversely, various features of the invention, which are, for brevity, described in the context of a single embodiment, may also be provided separately or in any suitable subcombination or as suitable in any other described embodiment of the invention. Certain features described in the context of various embodiments are not to be considered essential features of those embodiments, unless the embodiment is inoperative without those elements.

Various embodiments and aspects of the present invention as delineated hereinabove and as claimed in the claims section below find experimental support in the following examples.

EXAMPLES

Reference is now made to the following examples, which together with the above descriptions, illustrate the invention in a non limiting fashion.

Generally, the nomenclature used herein and the laboratory procedures utilized in the present invention include molecular, biochemical, microbiological and recombinant DNA techniques. Such techniques are thoroughly explained in the literature. See, for example, “Molecular Cloning: A laboratory Manual” Sambrook et al., (1989); “Current Protocols in Molecular Biology” Volumes I-III Ausubel, R. M., ed. (1994); Ausubel et al., “Current Protocols in Molecular Biology”, John Wiley and Sons, Baltimore, Md. (1989); Perbal, “A Practical Guide to Molecular Cloning”, John Wiley & Sons, New York (1988); Watson et al., “Recombinant DNA”, Scientific American Books, New York; Birren et al. (eds) “Genome Analysis: A Laboratory Manual Series”, Vols. 1-4, Cold Spring Harbor Laboratory Press, New York (1998); methodologies as set forth in U.S. Pat. Nos. 4,666,828; 4,683,202; 4,801,531; 5,192,659 and 5,272,057; “Cell Biology: A Laboratory Handbook”, Volumes I-III Cellis, J. E., ed. (1994); “Culture of Animal Cells—A Manual of Basic Technique” by Freshney, Wiley-Liss, N.Y. (1994), Third Edition; “Current Protocols in Immunology” Volumes I-III Coligan J. E., ed. (1994); Stites et al. (eds), “Basic and Clinical Immunology” (8th Edition), Appleton & Lange, Norwalk, Conn. (1994); Mishell and Shiigi (eds), “Selected Methods in Cellular Immunology”, W. H. Freeman and Co., New York (1980); available immunoassays are extensively described in the patent and scientific literature, see, for example, U.S. Pat. Nos. 3,791,932; 3,839,153; 3,850,752; 3,850,578; 3,853,987; 3,867,517; 3,879,262; 3,901,654; 3,935,074; 3,984,533; 3,996,345; 4,034,074; 4,098,876; 4,879,219; 5,011,771 and 5,281,521; “Oligonucleotide Synthesis” Gait, M. J., ed. (1984); “Nucleic Acid Hybridization” Hames, B. D., and Higgins S. J., eds. (1985); “Transcription and Translation” Hames, B. D., and Higgins S. J., eds. (1984); “Animal Cell Culture” Freshney, R. I., ed. (1986); “Immobilized Cells and Enzymes” IRL Press, (1986); “A Practical Guide to Molecular Cloning” Perbal, B., (1984) and “Methods in Enzymology” Vol. 1-317, Academic Press; “PCR Protocols: A Guide To Methods And Applications”, Academic Press, San Diego, Calif. (1990); Marshak et al., “Strategies for Protein Purification and Characterization—A Laboratory Course Manual” CSHL Press (1996); all of which are incorporated by reference as if fully set forth herein. Other general references are provided throughout this document. The procedures therein are believed to be well known in the art and are provided for the convenience of the reader. All the information contained therein is incorporated herein by reference.

General Materials and Methods

208 subjects (ages ranging from 21 to 40, no history of olfactory dysfunction, mean age 26.7±2.9, 110 women) participated in the intensity-rating (n=24), similarity scoring (n=59), discrimination (n=20), identification (n=25), delayed match-to-sample (n=60), pleasantness and edibility rating (n=20) experiments. All experiments were conducted in stainless-steel-coated odorant nonadherent rooms. All interactions with subjects during the experiments were via computer interface only. All experiments employed ˜40-second inter-trial intervals, and trial order was counter-balanced across subjects. Odorant mixtures were reprepared every two days. Table 1A-D provides the recipes for the mixtures of the similarity tests.

TABLES 1A-1D Table 1A-Set-1 Table 1B-Set-2 Table 1C-Set-3 Table 1D-Set-4   7   2 ^(#∧)   3 ^(#)   2   2 ^(#∧)*   3 ^(#∧)   1 ^(#∧$)   2 ^(#∧$)   9 ^(∧)   3   7   5 ^(#∧)  10 ^(#∧)*^(&)   5   5 ^(#∧)*^(&)   3 ^(∧)*  10 ^(#)   5 ^(#∧)*   9 ^(#)  13 ^(#)  16 *   7 ^(#∧)   9 ^(#)   7 ^(#∧$)  18 *  12 ^(#)  10 ^(∧)  18  20 ^(#∧)   9 ^(#∧)  10 ^(#$)  12 ^(#∧$)*^(&)  20 ^(#∧)*^(&)  13  12 ^(#)  20  23 ^(#)  12 ^(#∧)*  16 ^(#)  13 ^(#∧$&)  23 ^(#∧)  16 ^(#)  16 ^(#∧)  21 ^(#∧)  29 ^(∧)  13 ^(#)*^(&)  18 ^(#)  27 ^(#∧)  30 ^(#)  21 ^(#∧)*  23 ^(#)  27 ^(#)  34  18 ^(#)  20 ^(#$)  30 ^(#∧)  34 ^(#∧)*  27 ^(#∧)  30 ^(#)  29 ^(∧)  36 ^(∧&)  21 ^(#)  21 ^(∧$)  31 ^(#$)  37 ^(#)  29 ^(#∧)  31 ^(#)  37 ^(∧)  37 ^(#∧)  27 ^(#∧)  23 ^(#)  34  44 ^(#∧)  31 ^(#∧)  34 ^(#∧)*  38 ^(#∧)  38 ^(#∧)*  30 ^(#∧)  29 ^(#$)  49  49 ^(#∧)*  36 ^(#)*  36  40 ^(#)  39 ^(#)  31 ^(#)*  36 ^(#∧)  52 ^(#)  51  38 ^(#∧)*  39 ^(&)  44 ^(#)  40  47 ^(#∧)*  37 *  53 ^(#∧)  52 ^(#∧)  39 ^(#)*^(&)  47 ^(#)  49 ^(#)  44 ^(#&)  49  38 ^(#)  54 ^(#)  53 ^(∧)  40 ^(#∧)*  58 ^(∧)  51 ^(#)  51 ^(#∧)*  52 ^(#∧)  39 ^(#∧)  56 ^(#)  54 ^(#)  47 ^(#∧)  59 ^(#∧)*  52 ^(#∧)  54  53 ^(#)  40 ^(#∧)*  60 ^(#∧$)  56 ^(#∧)  58 ^(#)  61 ^(#∧)  53 ^(#∧)*  58 ^(#)  56 ^(#)  44  63  59 ^(#∧)*  60 ^(#)*^(&)  64 ^(#)  54 ^(#)  61 ^(#)*  59 ^(∧)  47 ^(#)  64 ^(#∧$)*  61  68 ^(#∧)  68 ^(#)*  56 ^(#)  70  60 ^(#)  50  68  63 ^(∧)  72 ^(#)  74 ^(#)*^(&)  60 *^(&)  72 *  63 ^(#)  51 ^(∧$)  71 ^(∧)  64 ^(#∧)*  81  75 ^(#)  63 ^(#∧)  75 ^(∧)  64 ^(#∧)  58 ^(#)  73 ^(#)  70 ^(#∧)*^(&)  82 ^(#∧)*^(&)  82 ^(#∧)*^(&)  70 ^(∧)*  77 ^(#∧&)  68 ^(&)  59 ^(∧$)  75 ^(∧)  71  93 ^(#)  89 ^(#∧)  71 ^(#)  82 ^(#∧)  71  61 *  81  73 ^(#)*  94  94 ^(#∧)*  72 ^(#∧)  83 ^(#)  73 ^(#∧)  70 ^(#∧$&)  82 ^(#∧)  74 ^(#)  95 ^(∧)  96 ^(#∧)*  73 ^(#)  93 ^(#)  74 ^(#)  72 ^(#∧$)*  83  75 ^(#)  96 ^(#∧) 100 ^(#)*^(&)  76 ^(#)  94 ^(#∧)*  76  74 ^(#∧)  93 ^(#$)  76 ^(#) 107 ^(#∧) 103  77 ^(∧)  95 ^(#)  81  76 ^(#∧)*  94 ^(#∧$)*  77 ^(#) 109 ^(#) 104 ^(#∧)  81 ^(∧)  97 ^(#)  89 ^(#∧)*^(&)  77 ^(∧$)  95 ^(#)  83 ^(#)* 110 107 ^(∧)  83 ^(#∧) 110 ^(#∧)  96 *  89 ^(#∧$)  96  89 ^(#∧&) 111 ^(#) 109 ^(#∧)  93 ^(#∧)* 111 ^(#∧) 100 ^(#)  97 100  97 ^(#∧) 112 ^(#)* 110 ^(#∧)  95 ^(#)* 114 103 102 ^(#∧) 104 ^(#∧$)* 100 ^(#∧) 118 ^(#) 111 *  97 ^(#∧) 115 ^(#∧) 104 ^(#∧) 103 110 ^(#)* 103 121 ^(#) 119 ^(#) 112 ^(∧)*^(&) 118 ^(#∧) 107 ^(#∧)* 107 ^(#$) 111 ^(#)*^(&) 104 ^(#) 126 ^(#∧) 121 ^(∧) 113 ^(#)* 119 ^(#∧)* 109 ^(#∧) 109 ^(#$) 115 ^(#$) 113 ^(#∧) 132 ^(∧)* 126 ^(#)* 114 ^(#∧)* 121 ^(#) 112 ^(#∧&) 112 ^(#) 118 ^(#∧$) 114 ^(#) 133 132 ^(#∧)* 115 ^(#∧)*^(&) 132 ^(#∧) 113 ^(∧)* 113 ^(#∧$)*^(&) 119 ^(#∧)* 115 * 134 ^(#) 133 ^(#∧) 117 ^(#)*^(&) 135 ^(#) 117 ^(#∧) 114 ^(#∧) 121 ^(#) 117 ^(#∧&) 135 ^(#) 134 ^(#) 118 ^(#) 138 ^(#) 126 ^(#)* 117 ^(#∧&) 132 ^(#$) 119 ^(#∧) 139 ^(∧) 139 ^(#∧) 135 ^(#∧) 139 ^(#∧) 133 ^(#∧) 126 * 133 ^(#∧&) 138 ^(#) 142 ^(∧) 141 ^(#∧) 138 ^(#) 141 ^(#∧)* 134 ^(#)* 127 ^(#∧$) 135 141 ^(#∧) 143 ^(#∧)*^(&) 142 143 ^(#∧) 143 ^(#) 142 ^(#∧) 131 138 ^(#) 134 139 ^(#∧$)* 140 ^(#∧)* 141 ^(#∧$) 143 ^(#∧) 142 ^(∧)

The odorants selected for each mixture in the similarity experiment (odorants listed by their numbers from Dravnieks atlas). Number of components: #30; {circumflex over ( )} 20; $ 15; * 10; & 4; Bold 1. The odors are identified by numbers according to Tables 2A-B. Set-1 and set-2, were constructed based on the perceptual space, set-3 and set-4, were selected based on the physicochemical space.

TABLE 2A List of odorants and concentrations On the first column are the ordinal numbers from Dravnieks ‘Atlas of Odor Character Profiles ^(1,2); On the six column odorants’ phase: L = liquid, S = solid. CID CAS v/v % or Name Number Number w(g)/v % Solvent 1 Abhexone 61199 698-10-2 0.01 S water 2 Acetophenone 7410 98-86-2 0.15 L mineral oil 3 ortho-Acetyl pyridine 14286 1122-62-9 0.001 L 1,2-propanediol 5 ethyl 3-methyl-3- 6501 77-83-8 1 L 1,2-propanediol phenyl 7 gamma-nonalactone 7710 104-61-0 0.5 L mineral oil 9 iso-amyl acetate 31276 123-92-2 0.1 L mineral oil 10 amyl butyrate 10890 540-18-1 1 L 1,2-propanediol 12 iso-pentyl phenyl 7600 102-19-2 1 L mineral oil acetate 13 pentyl valerate 62433 2173-56-0 50 L 1,2-propanediol 16 Anisole 7519 100-66-3 0.4 L mineral oil 18 Benzaldehyde 240 100-52-7 0.25 L mineral oil 20 iso-bornyl acetate 93009 5655-61-8 5 L 1,2-propanediol 21 butanoic acid 264 107-92-6 1.2 L water 23 butyl sulfide 11002 544-40-1 0.15 L mineral oil 27 Caryophyllene 5281515 87-44-5 15 L mineral oil 29 Celeriax 6259976 17369-59-4 0.2 L 1,2-propanediol 30 Chlorothymol 6982 89-68-9 saturated S 1,2-propanediol 31 cinnamic aldehyde 307 104-55-2 0.01 L 1,2-propanediol 34 Coumarin 323 91-64-5 saturated S mineral oil 36 p-Cresol 2879 106-44-5 1 S 1,2-propanediol 37 p-cresyl acetate 8797 140-39-6 0.03 L mineral oil 38 p-cresyl-iso-butyrate 7685 103-93-5 10 L mineral oil 39 4-methyl anisole 7731 104-93-8 0.15 L mineral oil 40 cuminic aldehyde 326 122-03-2 1 L mineral oil 44 Cyclohexanol 7966 108-93-0 0.4 L mineral oil 47 2,4-trans-trans- 5283349 25152-84-5 0.5 L mineral oil decadienal 49 dibutyl amine 8148 111-92-2 3 L 1,2-propanediol 50 diethyl sulfide 9609 352-93-2 0.01 L 1,2-propanediol 51 dimethyl benzyl 24915 10094-34-5 20 L 1,2-propanediol carbinyl butyrate 52 dimethyl phenyl ethyl 7632 103-05-9 saturated S 1,2-propanediol carbinol 53 2,3-dimethyl Pyrazine 22201 5910-89-4 0.2 L 1,2-propanediol 54 2,5-dimethyl Pyrazine 31252 123-32-0 0.3 L mineral oil 56 dimethyl trisulfide 19310 3658-80-8 0.001 L 1,2-propanediol 58 diphenyl Oxide 7583 101-84-8 1 S mineral oil 59 ethyl butyrate 7762 105-54-4 0.01 L mineral oil 60 ethyl propionate 7749 105-37-3 0.25 L mineral oil 61 2-ethyl pyrazine 26331 13925-00-3 0.4 L mineral oil 63 Eucalyptol 2758 470-82-6 2 L mineral oil 64 Eugenol 3314 97-53-0 0.3 L mineral oil 68 furfuryl mercaptan 7363 98-02-2 0.001 L 1,2-propanediol 70 Guaiacol 460 90-05-1 0.25 S mineral oil 71 Heptanal 8130 111-71-7 0.04 L mineral oil 72 1-heptanol 8129 111-70-6 0.4 L mineral oil 73 Hexanal 6184 66-25-1 0.1 L 1,2-propanediol 74 hexanoic acid 8892 142-62-1 2 L 1,2-propanediol 75 1-hexanol 8103 111-27-3 1 L mineral oil 76 3-hexanol 12178 623-37-0 0.4 L mineral oil 77 trans-1-Hexanal 5281168 6728-26-3 0.1 L mineral oil 81 2-phenyl 62336 90-87-9 15 L mineral oil propionaldehyde dimethyl acetal 82 hydroxy Citronellal 7888 107-75-5 50 L mineral oil 83 Indole 798 120-72-9 0.5 S 1,2-propanediol 89 Linalool 443158 126-91-0 2.5 L mineral oil 93 Melonal 61016 106-72-9 1 L mineral oil 94 1-menthol 16666 2216-51-5 3.5 S 1,2-propanediol 95 2-methoxy naphthalene 7119 93-04-9 saturated S mineral oil 96 methyl anthranilate 8635 134-20-3 0.35 L mineral oil 97 methyl acetaldehyde 20859 462-95-3 0.15 L 1,2-propanediol dimethyl acetal 100 para-methyl quinoline 7059 91-62-3 0.25 L 1,2-propanediol 102 methyl salicylate 4133 119-36-8 0.25 L mineral oil 103 S-(methyl thio) 62444 2432-51-1 0.03 L mineral oil butyrate 104 musk galaxolide 91497 1222-05-5 5 L 1,2-propanediol 107 nonyl acetate 8918 143-13-5 25 L mineral oil 109 1-octanol 957 111-87-5 0.75 L mineral oil 110 1-octen 3-ol 18827 3391-86-4 0.04 L mineral oil 111 pentanoic acid 7991 109-52-4 0.1 L mineral oil 112 4-Pentenoic acid 61138 591-80-0 0.3 L mineral oil 113 Phenyl acetic acid 999 103-82-2 10 S mineral oil 114 phenyl acetylene 10821 536-74-3 0.3 L mineral oil 115 phenyl Ethanol 6054 60-12-8 50 L mineral oil 117 iso-phorone 6544 78-59-1 3 L mineral oil 118 alpha-pinene 6654 80-56-8 15 L mineral oil 119 propyl butyrate 7770 105-66-8 1.3 L mineral oil 121 Propyl sulfide 8118 111-47-7 0.1 L mineral oil 126 Skatole 6736 83-34-1 0.005 S 1,2-propanediol 127 α-Terpineol 17100 10482-56-1 10 S 1,2-propanediol 131 thioglycolic acid 31277 123-93-3 10 5 water 132 Thiophene 8030 110-02-1 2.5 L mineral oil 133 Thymol 6989 89-83-8 13 S 1,2-propanediol 134 ortho-tolualdehyde 10722 529-20-4 0.1 L mineral oil 135 Toluene 1140 108-88-3 3 L mineral oil 138 gamma-undecalactone 7714 104-67-6 10 L mineral oil 139 undecylenic acid 5634 112-38-9 13 S 1,2-propanediol 140 iso-valeraldehyde 11552 590-86-3 0.0003 L mineral oil 141 iso-valeric acid 10430 503-74-2 0.01 L mineral oil 142 gamma-valerolactone 7921 108-29-2 0.2 L 1,2-propanediol 143 Vanillin 1183 121-33-5 5 S mineral oil

TABLE 2B List of 58 additional odorants and concentrations CID CAS v/v % or Name Number Number w/v % Solvent acetic acid 176 64-19-7 10 L water Acetaldehyde 177 75-07-0 5 L water 3-hydroxybutan-2-one 179 513-86-0 1.1 S 1,2-propanediol propan-2-one 180 67-64-1 25 L water Butanal 261 123-72-8 0.002 L water Octanal 454 124-13-0 0.25 L mineral oil 2-hydroxypropanoic acid 612 50-21-5 100 L butane-2,3-dione 650 431-03-8 0.5 L 1,2-propanediol propan-1-ol 1031 71-23-8 15 L water 2-oxopropanoic acid 1060 127-17-3 0.8 L water methylsulfanylmethane 1068 75-18-3 0.004 L water decanoic acid 2969 334-48-5 100 S 1,2-propanediol propan-2-ol 3776 67-63-0 100 L 2-methylpropanal 6561 78-84-2 0.025 L 1,2-propanediol butan-2-one 6569 78-93-3 1.5 L 1,2-propanediol methyl acetate 6584 79-50-9 7.5 L water 2-methylpropanoic acid 6590 79-31-2 1.25 L 1,2-propanediol methyl benzoate 7150 93-58-3 0.5 L 1,2-propanediol ethyl benzoate 7165 93-89-0 0.005 L 1,2-propanediol ethyl 2-hydroxypropanoate 7344 97-64-3 20 L 1,2-propanediol 2-methyl-5-propan-2- 7460 99-83-2 0.5 L mineral oil ylcyclohexa-1,3-diene 1-methyl-4-propan-2- 7463 99-87-6 0.5 L 1,2-propanediol ylbenzene 2-phenylethyl acetate 7654 103-45-7 100 L (3S)-3,7-dimethyloct-6-en-1- 7793 7540-51-4 1 L 1,2-propanediol ol ethyl octanoate 7799 106-32-1 1 L 1,2-propanediol propane-1-thiol 7848 107-03-9 0.0005 L 1,2-propanediol ethyl formate 8025 109-94-4 8.33 L 1,2-propanediol ethyl decanoate 8048 110-38-3 100 L heptan-2-one 8051 110-43-0 0.33 L 1,2-propanediol methyl octanoate 8091 111-11-5 3.5 L 1,2-propanediol Undecanal 8186 112-44-7 0.2 L 1,2-propanediol ethyl acetate 8857 141-78-6 3 L 1,2-propanediol 4-methylpent-3-en-2-one 8858 141-79-7 1 L 1,2-propanediol butane-2-thiol 10560 513-53-1 0.0005 L 1,2-propanediol ethyl pentanoate 10882 539-82-2 0.025 L 1,2-propanediol heptan-2-ol 10976 543-49-7 5 L 1,2-propanediol methyl propanoate 11124 554-12-1 2.5 L 1,2-propanediol hexan-3-one 11509 589-38-8 0.5 L 1,2-propanediol pent-1-en-3-ol 12020 616-25-1 0.3 L 1,2-propanediol methyl butanoate 12180 623-42-7 0.15 L 1,2-propanediol (methyldisulfanyl)methane 12232 624-92-0 0.025 L 1,2-propanediol pentyl acetate 12348 628-63-7 0.75 L 1,2-propanediol nonan-2-ol 12367 628-99-9 1 L 1,2-propanediol decan-2-one 12741 693-54-9 100 L bis(methylsulfanyl)methane 15380 1618-26-4 0.002 L 1,2-propanediol pentan-2-ol 22386 6032-29-7 4 L 1,2-propanediol hexyl hexanoate 22873 6378-65-0 26.66 L 1,2-propanediol 4-methoxybenzaldehyde 31244 123-11-5 3.3 L 1,2-propanediol diethyl butanedioate 31249 123-25-1 100 L ethyl hexanoate 31265 123-66-0 0.5 L 1,2-propanediol butyl acetate 31272 123-86-4 1 L 1,2-propanediol 4-ethyl guaiacol 62465 2785-89-9 0.1 L 1,2-propanediol (R)-(+)-beta-citronellol 101977 1117-61-9 100 L Limonene 440917 5989-27-5 5 L mineral oil laevo-beta-pinene 440967 18172-67-3 10 L 1,2-propanediol Geraniol 637566 106-24-1 50% L 1,2-propanediol Thiolane 1127 110-01-1 0.0005 L 1,2-propanediol trimethyl amine 1146 75-50-3 0.025 L water

Equated-intensity odorants: All odorants were purchased from Aldrich Chemicals (St. Louis, Mo.) in the highest available purity. All odorants were diluted with either mineral oil, 1,2-propanediol or deionized distilled water to a point of approximately equally perceived intensity. This perceived-intensity equation was conducted according to previously published methods (Khan et al., 2007, The Journal of Neuroscience 27, 10015 (2007)). In brief, the present inventors identified the odorant with lowest perceived intensity, and first diluted all others to equal perceived intensity as estimated by experienced lab members. Next, 24 naïve subjects (10 females) smelled the odorants and rated their intensity. The present inventors then further diluted any odorant that was 2 or more standard deviations away from the mean intensity of the series, and repeated the process until we had no outliers. This process is suboptimal, but considering the natural variability in intensity perception, together with naive subjects' bias to identify “a difference”, and the iterative nature of this procedure, any stricter criteria would generate an endless process.

The odorants used and their final dilutions are listed in Tables 2A-B.

Selecting components for mixtures in the similarity experiments: The present inventors selected mixtures of odorants out of the initial pool of odorants in a pseudo-random manner, which were applied to two separate databases. The first database consists of 138 odorants, each having 146 perceptual descriptors in the Dravnieks' atlas (Dravnieks, A. Atlas of odor character profiles (ASTM Press, P A, 1985); Dravnieks, A. Odor quality: semantically generated multi-dimensional profiles are stable. Science 218, 799-801 (1982)), and this database is referred to as the “odor perceptual space”. The second database contains 1492 odorants commonly used in olfaction research (Khan, R. M., et al. Predicting odor pleasantness from odorant structure: pleasantness as a reflection of the physical world. The Journal of Neuroscience 27, 10015 (2007)). For each of the 1492 odorants the present inventors obtained 1438 physicochemical descriptors using Dragon software (Talete, Milan, Italy) (Todeschini, R. & Consonni, V. Molecular descriptors for chemoinformatics (Vch Pub, 2009).

The values of the 1438 physicochemical descriptors are on vastly different scales. Thus, to eliminate this source of variance, the values of each property were z-scored. This data is referred to herein as the “odor physiochemical space”. Each odorant was represented by a vector whose coordinates describe its 146 perceptual or 1438 physicochemical properties. The selection process of the different mixture sizes from the present collection of odorants was as follows: the distance between an odorant x and a mixture G was defined as the minimum Euclidean distance between the odorant vector and the vector of one of the mixture's components: D(x,G)=min_(gϵG) d(x,g) where d(x,g) is the Euclidean distance between the vectors x and g. For example, if the coordinates of the odorants in a mixture G={g:gϵG} of a hypothetical database were ([4,−6,8],[3,−3,10],[−4,0,3]), then the distance of an odorant x with coordinates [−2,1,1] to that mixture would be 3, because that is the distance to the closest point: [−4,0,3].

Next, the present inventors defined a measure T(G) of how well a collection of odorants is spread out over the database of odorants. T(G) is the average distance between the mixture and all of the odorants in the database (138 in the perceptual space and 1492 in the physicochemical space):

${T(G)} = \frac{\sum{D\left( {x,G} \right)}}{X}$ The present experiment required that two disjoint collections of 40 odorants were found, which are well distributed over the database. From a pool of 80 odorants (picked randomly out of the 86), 10,000 random samples of 40 were selected, and for each sample G, T(G)+T(G^(C)) was calculated, where G^(C) represents the complementary set of G. The present inventors selected the collection G which gave the lowest sum of T(G)+T(G^(C)) and labeled it 40A and its complement G^(C) as 40B. Next, the present inventors another 10,000 samples of 30 odorants out of 40A and picked the one sample G such that T(G) was minimal and labeled it 30A. In a similar manner 20A, 10A, and 4A were picked. 1A was picked randomly out of the odorants in 40A. The procedure was repeated to create the subsets 30B, 20B, 10B, 4B, and 1B, out of 40B. The mixtures described above constitute one set that was used to conduct an experiment with a group of ˜14 subjects. A total of four different sets of mixtures were prepared using this semi random process, each created for a separate experiment. Two of the sets, which will be referred to as set-1 and set-2, were constructed based on the perceptual space (138 vectors with 146 coefficients). The two other sets, set-3 and set-4, were selected based on the physicochemical space (1492 vectors with 1438 coefficients). In set-4, the largest mixture was composed of 43 components rather than 40, out of the full pool of 86 odorants, and the resulting mixture sizes in this set were 43, 30, 20, 15, 10, 4 and 1.

Pairwise similarity tests: In each experiment, each subject was presented with 48 comparisons of mixtures and was asked to rate their similarity on a visual-analogue scale ranging from 1 to 9, with 9 being the most similar. In total, there were 191 pairwise similarity comparisons, 147 of which were between non-overlapping mixtures and the remaining 44 were comparisons of mixtures to themselves. Each subject repeated the experiment on two different days to assess test-retest reliability. The ratings by subjects whose similarity ratings for identical mixtures were poorer by at least 2 standard deviations from the mean were discarded. This amounted to 3 subjects, retaining in the analysis: 12, 13, 13 and 18 subjects for each experiment. The average rated similarities were calculated across subjects.

Three-alternative forced-choice discrimination test: Twenty subjects (10 females) were presented with odorant triplets. Two of the jars in each triplet contained an identical 35-component mixture (4 versions across experiments), and the third jar contained a different non-overlapping mixture of various component sizes which span the perceptual space (2, 3, 4, 5, 7, 8, 10, 14, 15, 17, 20, 25 and 30 components). The subjects were asked to pick out the jar that contained the dissimilar mixture and rate their confidence level on a scale of 1 to 5, with 5 corresponding to most certain. The subjects were enabled to smell each of the three jars twice. Each subject repeated the experiment on two different days to assess test-retest reliability. Correct discrimination scored 1 and incorrect discrimination scored 0. The scores for each mixture component number were averaged across the four versions (FIG. 2B). The confidence level ratings of subjects who answered correctly in less than 70% of the discrimination trials were discarded, retaining 16 subjects for this analysis. The results were normalized to a scale of 0 to 100 (FIG. 2C), with 100 corresponding to most certain.

Identification experiments: The mixtures were selected to span the physicochemical space. After a two-day acquaintance with 40-component Laurax, 12 subjects performed a four-alternative forced-choice identification task for 23 different novel target odorant mixtures ranging from 1, 4, 10, 20, 30, to 40 components, all selected to span physicochemical space. Subjects were asked to choose the most appropriate label for each mixture out of four options; one being Laurax, and the other three were labels provided by a perfumer. In the second experiment, 13 different subjects had to choose the appropriate label for the same mixtures out of five options, four the same as the above, and the fifth option was “other”. Choosing Laurax scored 1 and choosing any of the other names scored 0. The number of times Laurax was chosen was averaged across four versions of each number of components in a mixture (three versions in the case of 40 component mixture). Two out of the 25 subjects were excluded because they did not identify the very same mixture they smelled in the two previous days as Laurax. Thus, the number of subjects for further analysis was 11 and 12, respectively.

Delayed match-to-sample: After two-day acquaintance with 30-component Laurax, 12 subjects smelled 21 target mixtures of various component numbers (30, 25, 20, 15, 10, 5 and 1), which were selected out of the remaining 56 odorants. In other words, those mixtures shared no components in common with the Laurax mixture. Subjects were asked to decide if the smell of each jar was Laurax, by pressing ‘Yes’ or ‘No’. All subjects correctly identified Laurax as Laurax in all 6 repetitions, with the exception of one subject who correctly identified it in 4 of 6 repetitions.

In the second delayed match-to-sample test, after a two-day acquaintance with the 25-component Laurax mixture, 16 subjects smelled 17 target mixtures from a pool of 144 single molecules: nine mixtures whose components spanned physicochemical space, five mixtures whose components were clustered in this space, and two mixtures that had the same components as the Laurax they learned, however, one single molecule provided 50% of the total mixture solution volume: in the first mixture this molecule was isopropyl alcohol (67-63-0) and in the second mixture, this molecule was ethyl butyrate (105-54-4).

Subjects were asked to answer whether the odor was Laurax using a VAS ranging between “yes” and “no”. The present inventors considered answers above 75% as yes, and below 25% as no.

The same paradigm and analysis were used in the two final delayed match-to-sample tasks using mixtures of 15 (18 subjects) and 60 (14 subjects) components (Table 4: 60A). Two subjects were excluded from further analysis, one did not identify the very same mixture smelled in the two previous days as Laurax, and the second subject did not use the VAS scale.

Example 1 Mixtures with Many Equal-Intensity Spanned Components Begin to Smell the Same

86 monomolecular odorants were obtained that were well distributed in both perceptual (1, 11-13) (FIG. 1A) and physicochemical (1, 14, 15) (FIG. 1B) stimulus space. Each of these odorants were diluted separately to a point of about equal perceived intensity as estimated by an independent group of 24 subjects (Table 2A, herein above), and various odorant mixtures containing various numbers of such equal-intensity odorant components were prepared. Importantly, to prevent formation of novel compounds, odorant mixtures were not mixed in the liquid phase, but rather each component was dripped onto a common absorbing pad in a sniff-jar, such that their vapors alone mixed in the jar headspace. To select the components of each mixture, an algorithm was used that automatically identified combinations of molecules spread out in olfactory stimulus space. Several different versions for each mixture size were prepared containing 1, 4, 10, 15, 20, 30, or 40/43 components, such that half of the versions were optimally spread in perceptual space, and half of the versions were optimally spread in physicochemical space.

Pairwise similarity tests (using a 9-point visual analogue scale; VAS) of 191 mixture pairs, in 56 subjects (average of 14 subjects per comparison) were performed. Each target mixture (1, 4, 10, 15, 20, 30, or 40/43 components) was compared to all other mixtures (1, 4, 10, 15, 20, 30, or 40/43 components), and as a control, to itself. Other than comparisons of a mixture to itself, all comparisons were non-overlapping, i.e. each pair of mixtures under comparison shared no components in common.

Consistent with the present hypothesis, there was a significant relation between the number of components in each of two mixtures and their perceived similarity, in both perceptual (F(1,17)=124.8, p<0.0001) and physicochemical (F(1,28)=34.1, p<0.0001) space, reflecting increased similarity with increased number of components (r=0.94 in perceptual space and r=0.75 in physicochemical space (both p<0.0001)) (FIG. 2A). Looking at each target mixture size independently revealed that this was consistently true for target mixtures of 20 or more components (correlation between number of components and similarity score, all r>0.58, all p<0.03, FIGS. 6A-D, FIGS. 7A-C), but not for target mixtures of fewer than 10 components (all r<0.18, all p>0.45, FIGS. 6F-H, FIGS. 7E-F). In other words, the more components in each of two mixtures, the more similar smelling those two mixtures became, despite sharing no components in common (FIG. 2A). This trend implies that if more and more nonoverlapping components are added to each of two mixtures, these two mixtures should eventually smell the same, despite sharing no components in common. Indeed, given a sufficient number of equal-intensity spanned components, this implies that all mixtures should eventually smell the same. This predicted ultimate point of perceptual convergence is referred to herein as olfactory white.

In order to ascertain whether the ˜30-component mixtures tested could be referred to as olfactory white, the present inventors conducted a discrimination experiment. Twenty subjects performed a three-alternative forced-choice discrimination task between a grand mixture made of 35 components and non-overlapping component mixtures of various sizes. Even when selecting the mixtures spread in perceptual space, the mixtures remained discernible. Although as the number of components increased discrimination accuracy decreased (Kendall Correlation (KC), Tau=−0.51, Z=2.13, p<0.04. FIG. 2B), as did self-rated confidence in the discrimination (KC, Tau=−0.76, Z=3.4, p<0.001. FIG. 2C), discrimination accuracy for even 30-component mixtures remained well above chance.

Example 2 Mixtures with Many Equal-Intensity Spanned Components are Identified as Olfactory White

Using vision, humans can easily discriminate between many different “whites”, yet they all retain the color-gestalt identity of white. To determine whether odorant mixtures of ˜30 spanned components similarly obtain a gestalt identity, an odor identification experiment was conducted. Selecting from physicochemical space, four versions of 40-component mixtures were generated. In order to prevent any cognitive influences of the label white, these mixtures were labeled with the meaningless name Laurax. Each of the four versions of Laurax was assigned to three different subjects from a group of 12. To acquaint themselves with the odor, each subject came to lab on three consecutive days, and every day repeatedly smelled and rated the applicability of 146 verbal descriptors (16) to their version of Laurax only. On the fourth day, test day, subjects performed a four-alternative forced-choice identification task for 23 different novel yet partially overlapping target odorant mixtures, ranging from 1, 4, 10, 20, 30, to 40-components, all selected to span physicochemical space. Each target mixture was provided with four alternative labels: Three labels were assigned by an expert perfumer (who was blind to experimental aims and conditions) as optimal identifiers for each mixture (Table 3, hereinbelow), and the fourth label was Laurax.

TABLE 3 Three perfumer-provided names for each of the mixtures 40 Version I 40 Version II 40 Version III 40 Version IV First Thyme Cherry Candy Vanilla Butter Cherry Candy experiment Vanilla-Cinnamon Vanilla Butter Animalistic Vanilla Butter Coconut Cinnamon Urine Cinnamon Second Oregano Red fruit Vanilla baking Cinnamon experiment Cinnamon Vanilla baking Cinnamon Tea Nutty Cherry Candy Cinnamon Tea Butter Butter 30 Version I 30 Version II 30 Version III 30 Version IV First Jasmine Beurre noisette Bengay Cherry Candy experiment Red fruit Cinnamon stick Thyme Feces Vinegar Vanilla Vanilla Butter Urine Second Air freshener Vanilla baking Oregano Cherry Candy experiment Green Apple Coffee Cinnamon Nutty Cinnamon Nutty Butter Vanilla baking 20 Version I 20 Version II 20 Version III 20 Version IV First Jasmine Patchouli-Vanilla Cat feces Patchouli- experiment Lily of the Valleys Coconut Jasmine Vanilla Water vapor Butter Animalistic Cinnamon Urine/Feces Second Nutty Nutty Ink Cinnamon experiment Vanilla baking Seasoned-Sweet Cat urine Apple Butter Butter Cinnamon Feces 10 Version I 10 Version II 10 Version III 10 Version IV First Cat urine Talc Cream Oregano Naphthalene experiment Feces Baby smell Vinegar Jasmine Oregano Urine butter Rose water Second Oregano Fruity peppermint Naphthalene experiment Urine/Feces Flowery Oregano Feces/Cat urine Rotten eggs Butter Feces Vinegar 4 Version I 4 Version II 4 Version III 4 Version IV First Oregano Feces Banana Oregano Pizza experiment Pizza Cat urine Cat urine Butter Cat urine Wild cat/Street cat Feces Vinegar Second Weedy Diluted Tomato paste Cider Thyme experiment Seasoned Fruity Cherry Candy Oregano Peppermint Sour Banana Pizza Sauce 1 Version I 1 Version II 1 Version III 1 Version IV First Coconut Naphthalene Banana Rose water experiment Bitter almond Narcissus Pear Nectar Blancmange Butter Jasmine Cherry Candy Sweet almond Second Coconut Flowery Banana Rose water experiment Fruity naphthalene Green Apple Pine needles Creamy medicinal Cherry candy Green Root

It was found that the probability of assigning the name Laurax to a novel mixture increased as the number of components increased (KC, Tau=0.73, Z=2.1 p<0.04) (FIG. 3A). Moreover, novel target mixtures with less than 20 components were significantly less likely to be identified as Laurax than novel targets of 20 components or more (t(10)=5.54, p<0.001. FIG. 3A inset). Finally, whereas chance application of a label in the four-alternative task is 25%, the label Laurax was applied to novel 40-component mixtures 57.6% of the time (t(10)=3, p<0.02).

The above-used descriptors for Laurax, although provided by a professional perfumer, may nevertheless lack universal applicability. To address the possibility of “dumping” (17), namely assignment of inappropriate labels in the face of limited alternatives, the experiment was repeated with a new group of 13 subjects, and the additional response option of ‘other’. Again, the probability of assigning the name Laurax increased as the number of components increased (KC, Tau=0.93, Z=2.6, p<0.01) (FIG. 3B), and novel target mixtures with less than 20 components were significantly less likely to be identified as Laurax than novel targets of 20 components or more (t(11)=5.68, p<0.001. FIG. 3B inset). Finally, whereas chance application of a label in the five-alternative task is 20%, the label Laurax was applied to novel 40-component mixtures 50% of the time (t(11)=3.35, p<0.007).

Because the present inventors were limited by the available components for which they had equated intensity, yet wanted to have meaningful differences across the various target mixtures, in the two above experiments there was inevitable minimal component overlap between the learned Laurax and target mixtures. Moreover, despite the addition of “other” as a viable response, “dumping” remains possible. With these considerations in mind, in the following experiments an even stricter test was applied:

After two-day acquaintance with 30-component Laurax, 12 subjects smelled 21 target mixtures of various component numbers, yet with zero component overlap to the Laurax they learned, and judged whether these mixtures were, or were not Laurax, i.e., no alternative labels were provided in this delayed match-to-sample task.

Consistent with the previous experiments, the probability of discriminating a mixture from the Laurax they learned decreased as the number of components increased (KC Tau=−0.67, Z=2.12, p<0.04. FIG. 4A), and novel target mixtures with less than 20 components were significantly more likely to be discriminated from Laurax than novel targets of 20 components or more (t(11)=3.49, p<0.006. FIG. 4A inset).

Moreover, chance in this task is 50%. Whereas subjects could easily discriminate smaller novel mixtures from the Laurax they learned (1, 5, 10, 15 components mixtures, all t>3.73, all p<0.005), they could not discriminate novel 30-component mixtures from the Laurax they learned (32.3%±33.7, t(11)=1.81, p>0.1). It is important to acknowledge the perceptual memory component in the delayed match-to-sample task. Had subjects been simultaneously provided with the Laurax they previously learned and the novel 30-component mixtures, they would likely discriminate them (as in FIG. 2B). However, given the gestalt percept of the Laurax they learned alone (which was indeed sufficient for 97% identification of the Laurax they learned), novel 30-component mixtures were deemed not significantly different from Laurax.

One may raise the possibility that Laurax became a percept associated with “large mixtures”, regardless of the mixtures' olfactory identity. To test this, the present inventors repeated the above strict delayed match-to-sample task in an additional 16 subjects, yet as test targets used only 25-component mixtures, nine versions that spanned physicochemical space, five versions that were clustered in physicochemical space, and two versions that were identical to the Laurax they learned, but components were not equated for perceived intensity. Because 86 molecules that span space were initially selected, now in order to generate 25-component clusters, the present inventors were forced to equate perceived intensity of an additional 58 molecules, such that they obtained a pool of 144 molecules to choose from (Table 2B, herein above). Moreover, to allow a distribution of results, rather than a yes-no selection, the present inventors asked subjects whether the odor was Laurax, and provided participants with a VAS ranging between “yes” and “no”. Answers above 75% were considered as yes, and below 25% as no. Novel clustered mixtures were more discernible from Laurax than novel spanned mixtures (clustered=67.5%±32.56 vs. spanned=42.36%±32.77: t(15)=2.89, p<0.02) (FIG. 4B), and the Laurax not equated for intensity was discernible from Laurax itself (because there is only one version of Laurax itself, here the exact binomial test was used, p<0.0002) (FIG. 4B). Thus, the percept of Laurax was more dependent on spanning olfactory space than on equating intensity. Moreover, it should be stressed that the true influence of spanning olfactory space and equating intensity is likely much greater than the significant interaction revealed here. That is because the possibility of selecting independent clusters sized 25 out of the 144 molecules for which intensity was equated remains limited. Thus the “clusters” were nevertheless quite spanned.

Thus, this result, although significant in of itself (p<0.02), likely underestimated the power of the manipulations.

Finally, to again verify the limits of this phenomenon, the present inventors repeated the delayed match-to-sample task in 18 additional subjects, testing mixtures of up to 15 components only, i.e., mixtures that on average should not converge. Here, 15-component clusters and 15-component spreads did not significantly differ from each other (t(17)=0.33, p=0.74), and both differed from the stored representation of the Laurax they learned (both p<0.001). In turn, the delayed match-to-sample task was repeated in 14 additional subjects, testing mixtures of up to 60 components, and similar results were obtained to those with 30 components, whereby participants could not discriminate novel 60-component mixtures from the stored representation of the 60-component Laurax they learned (exact binomial test, p>0.43). Taken together, the above experiments are consistent with the notion of a gestalt percept following combinations of ˜30 equal-intensity components or more that are well distributed in physicochemical space. This percept is referred to herein as olfactory white.

Example 3 Olfactory White Smells Intermediately Pleasant and Edible

The present dilution procedure, whereby all components were equated in magnitude with the least-intense component, rendered mixtures of overall low intensity. In other words, olfactory white was in no way overwhelming. But how did it smell? Perhaps tellingly, the descriptors of white provided by the professional perfumer were quite variable.

This can be taken to imply that white doesn't smell like any common object in particular. To provide a better notion of what olfactory white does smells like, the present inventors summarized the applicability of 146 verbal descriptors provided by the 85 subjects who smelled white repeatedly throughout all experiments (Table 4, hereinbelow).

TABLE 4 Verbal descriptors of White The 146 descriptors were provided by the 85 subjects who smelled white in all of the identification and delayed match-to-sample experiments. P.A is the percentage of applicability (geometric mean of the percent usage and percentage of the maximum possible score). Descriptors P.A. 1 Fruity, citrus 56.2 2 Lemon 45.9 3 Grapefruit 40.4 4 Orange 39.5 5 Fruity, other than citrus 53.7 6 Pineapple 34.3 7 Grape juice 30.7 8 Strawberry 21.1 9 Apple 27.5 10 Pear 28.8 11 Melon 30.7 12 Peach 28.3 13 Banana 19.0 14 Floral 62.1 15 Rose 43.2 16 Violets 24.8 17 Lavender 39.9 18 Cologne 40.1 19 Musky 45.5 20 Perfumery 63.6 21 Fragrant 72.9 22 Aromatic 62.3 23 Honey 26.4 24 Cherry 24.3 25 Almond 19.4 26 Nail polish remover 37.2 27 Nutty 19.3 28 Spicy 15.0 29 Clove 23.6 30 Cinnamon 24.1 31 Laurel leaves 18.6 32 Tea leaves 23.9 33 Seasoning (for meat) 5.5 34 Black pepper 7.1 35 Green pepper 5.9 36 Dill 11.4 37 Caraway 6.7 38 Oak wood, cognac 32.3 39 Woody, resinous 31.7 40 Cedarwood 16.8 41 Mothballs 28.9 42 Minty, peppermint 30.9 43 Camphor 31.8 44 Eucalyptus 39.7 45 Chocolate 7.4 46 Vanilla 30.4 47 Sweet 56.8 48 Maple syrup 22.2 49 Caramel 20.0 50 Malty 16.1 51 Raisins 20.2 52 Molasses 25.6 53 Coconut 19.3 54 Anise (licorice) 27.1 55 Alcoholic 43.5 56 Etherish, anesthetic 40.1 57 Cleaning fluid 49.8 58 Gasoline, solvent 12.5 59 Turpentine (pine oil) 21.7 60 Geranium leaves 33.4 61 Celery 8.5 62 Fresh green vegetables 9.0 63 Crushed weeds 15.2 64 Crushed grass 14.9 65 Herbal, green, cut grass 18.1 66 Raw cucumber 10.8 67 Hay 13.0 68 Grainy (as grain) 9.6 69 Yeasty 11.8 70 Bakery (fresh bread) 5.3 71 Sour milk 15.5 72 Fermented (rotten) fruit 23.3 73 Beery 13.0 74 Soapy 60.7 75 Leather 13.3 76 Cardboard 10.4 77 Rope 6.7 78 Wet paper 10.0 79 Wet wool, wet dog 8.9 80 Dirty linen 9.2 81 Stale 24.2 82 Musty, earthy, moldy 8.0 83 Raw potato 7.3 84 Mouse-like 6.0 85 Mushroom 10.1 86 Peanut butter 7.0 87 Beany 3.8 88 Eggy (fresh eggs) 7.2 89 Bark, birch bark 25.3 90 Cork 16.2 91 Burnt, smoky 6.4 92 Fresh tobacco smoke 15.2 93 Incense 44.2 94 Coffee 4.8 95 Stale tobacco smoke 17.4 96 Burnt paper 2.9 97 Burnt milk 5.4 98 Burnt rubber 12.3 99 Tar 5.1 100 Creosote 8.2 101 Disinfectant, carbolic 51.3 102 Medicinal 56.1 103 Chemical 66.1 104 Bitter 29.3 105 Sharp, pungent, acid 40.2 106 Sour, vinegar 35.1 107 Sauerkraut 14.9 108 Ammonia 20.0 109 Urine 10.6 110 Cat urine 6.7 111 Fishy 2.9 112 Kippery (smoked fish) 2.5 113 Seminal, sperm-like 7.7 114 New rubber 28.1 115 Sooty 4.3 116 Burnt candle 8.5 117 Kerosene 9.4 118 Oily, fatty 15.9 119 Buttery, fresh butter 10.5 120 Paint 32.2 121 Varnish 41.6 122 Popcorn 2.8 123 Fried chicken 2.0 124 Meaty (cooked, good) 1.9 125 Soupy 7.7 126 Cooked vegetables 7.4 127 Rancid 20.6 128 Sweaty 11.3 129 Cheesy 9.3 130 Household gas 12.3 131 Sulfidic 10.2 132 Garlic, onion 6.0 133 Metallic 19.5 134 Blood, raw meat 7.3 135 Animal 4.7 136 Sewer odor 4.8 137 Putrid, foul, decayed 17.8 138 Fecal (like manure) 6.2 139 Cadaverous (dead animal) 9.8 140 Sickening 22.1 141 Dry, powdery 22.3 142 Chalky 15.9 143 Light 48.4 144 Heavy 38.6 145 Cool, cooling 48.9 146 Warm 30.6

Moreover, 20 subjects were asked to rate 20 different odorants along VAS scales representing the two principal axes of human olfactory perception: One axis ranging from very pleasant to very unpleasant, and the other axis ranging from highly edible to highly poisonous (1, 13, 18). Of the 20 odorants they rated, 12 were previously well-characterized monomolecular odorants that span the first principal component of perception, four were previously used versions of 40-component olfactory white, and four were 10-component mixtures which provided maximal span of olfactory space. Whereas the pleasantness and edibility/poisonousness of the 20 odorants ranged from 0.10 to 0.86 and 0.11 to 0.70 respectively (after parsing the VAS to the range 0-1), the average pleasantness and edibility/poisonousness of olfactory white was 0.46±0.08 and 0.37±0.05 respectively (FIGS. 5A,B). In other words, olfactory white was largely intermediate along the key axes of human olfactory perception.

Example 3 Olfactory White is a Gestalt that Persists Over Time

To ask whether the percept of White is maintained over time, 20 participants from the above studies were re-invited to participate in an additional study conducted about six months after their initial and only acquaintance with Laurax. Moreover, in this experiment the present inventors also asked whether Laurax can mask other odors. Subjects were presented with 12 odorants, each delivered twice. Four of the odorants were the monomolecules most prominent in rose (10), a fifth odorant was a rose mixture made of these four monomolecules combined (Table 5, hereinbelow), five odorants were 30-component mixtures (white) which each contained these four monomolecules mixed with 26 additional molecules selected to span physicochemical space (Table 6, hereinbelow), and the remaining two odorants were the monomolecules isoamyl acetate and S-(methylthio) butyrate. Subjects were asked to identify the odorants in a 7-alternative (10 subjects) or 4-alternative (10 subjects) forced-choice identification task, where the alternatives always included: Rose, Laurax, Other, and either one (4AFC) or four (7AFC) additional odor names, selected to best fit the monomolecules used.

TABLE 5 The four main components of a rose which yield the rose odor The most prominent labels which were given by independent group of ten people were: rose, narcissus, geranium, lemon and citrus blossoms. CID Cas % of the rose Molecule name Number Number mixture β-phenylethyl alcohol  60-12-8 6054  98% Geraniol 106-24-1 637566 1.4% Citronellol 106-22-9 8842 0.3% Nerol 106-25-2 643820 0.3%

TABLE 6 Five versions of 30-components mixture The four CID numbers of the rose in bold and the complementary 26 molecules CID numbers for ideal spanning of the physicochemical space. Version I Version II Version III Version IV Version V 177 179 179 176 179 264 261 261 180 1060 307 307 307 454 1127 460 460 612 6054 2879 1031 2969 3314 6544 6054 6054 6054 4133 6654 6561 4133 6569 6054 7059 6736 5634 6584 6584 7344 7059 6184 6989 7685 7460 7460 6501 7654 7793 7654 7583 7165 7685 7799 7685 7685 7654 7793 7921 7731 7714 7731 8048 8048 7799 8048 8091 8130 8103 7966 8118 8118 8148 8842 7991 8139 8842 8842 10882 8048 8186 8858 8892 11002 8139 8842 12180 12348 12180 8186 8858 17100 16666 12232 8842 10882 22873 24915 12741 16666 10890 24915 31249 16666 20859 12180 31272 31272 17100 24915 16666 62444 31276 20859 26331 24915 62465 62433 24915 31265 31244 93009 91497 91497 31272 31276 440917 93009 93009 31276 62444 443158 440917 440967 62465 93009 637566 637566 637566 637566 443158 643820 643820 643820 643820 637566 5281515 5281515 5283349 5281168 643820

Despite the brief and limited past exposure to Laurax, subjects largely maintained the percept in mind. Whereas chance selection of Laurax was 14.3% in the first experiment, and 25% in the second, it was correctly selected 54% of the time in the first experiment (t(9)=7.96, p<0.001), and 65% of the time in the second experiment (t(9)=5.05, p<0.001). Moreover, the percept of Laurax largely overshadowed the percept of rose. Specifically, across both experiments the mixture of rose was correctly identified 70% of the time, yet only twice out of 40 trials (5%) was it labeled Laurax (419)=7.25, p<0.001). In contrast, Laurax (which contained the four molecules of rose) was correctly identified 59.5% percent of the time, yet only 14 times out of 200 trials (7%) was it labeled rose (419)=9.52, p<0.001) (full results of this experiment are in FIG. 8). In other words, subjects remembered the percept of white, and it served to effectively obscure the percept of rose.

DISCUSSION

As illustrated in FIG. 2A: the more components two mixtures have, the more similar they smell, despite sharing no individual components in common. Moreover, odorant-mixtures with ˜30 components or more begin to smell alike, having a quality referred to herein as olfactory white. Large mixtures in the real world, such as wine, coffee, and rose, do not all smell similar and ‘white’ for two reasons: The first is that unlike the olfactory whites generated herein, the components of wine, coffee and rose were not designed to span stimulus space. As seen in FIG. 4B, if mixture components are less spanned, the ensuing odorant is different from white. The second consideration is that unlike the olfactory whites generated herein, wine, coffee and rose do not contain equal intensity components. As seen in FIG. 4B, if mixture components are not equated for intensity, the ensuing odorant also smells less white. Such imbalance is typical in natural mixtures. For example, computing the spread of the 63 key components of rose (10) in physicochemical space reveals that they reflect an extreme cluster, whereas the 60-component whites generated herein (Laurax) reflected an average span (see FIG. 9 for this and additional examples). Moreover, one of the components of rose alone, phenylethyl alcohol, contributes ˜70% of rose headspace (19), and indeed this component alone generates a poor-quality but unmistakable smell of rose. In other words, a natural olfactory object such as rose contains components that are clustered in olfactory space, and of unequal intensity, and therefore doesn't smell white.

Olfaction is considered a synthetic rather than analytical sensory system (20-24). For example, humans are very poor at identifying components in a mixture, even when they are familiar with the components alone (5-7). Similarly, cortical patterns of neural activity induced by a mixture are unique, and not a combination of neural activity induced by the mixtures' components (25-29). Moreover, the pattern of neural activity in the olfactory bulb following a natural object typically reflects the pattern associated with the dominant monomolecular odorant (alone) associated with that object (30). In other words, the olfactory system treats odorant-mixtures as unitary synthetic objects, and not as an analytical combination of components (20-24, 28, 29, 31). The current results are consistent with this notion, and the ˜30 component phenomenon therefore implies computational boundaries for olfactory receptors, bulb, cortex, and perception.

Example 4 Exemplary Mixtures

Exemplary mixtures according to some embodiments of the present invention are provided in Tables 7A-C below. Each of Tables 7A and 7B lists preferred odorant components for two odorant mixtures with 60 components, and Table 6C lists preferred odorant components for an odorant mixture with 30 components. The tables include lists of odorant components identified by their CID numbers.

TABLE 7 Table 7A Table 7B Table 7C 177 176 264 179 240 307 180 264 454 261 307 1031 326 798 6054 460 1031 6736 612 2758 7165 999 2969 7460 1060 4133 7600 1068 6054 7685 1127 6184 7793 1140 6501 7848 1146 6590 7888 2879 6736 7966 3314 6989 8025 3776 7059 8048 5634 7150 8091 6544 7165 8118 6561 7363 8797 6654 7410 8918 6982 7600 10722 7344 7685 12232 7463 7731 16666 7519 7749 22201 7583 7762 31272 7632 7793 31276 7654 7848 61138 7710 7888 91497 7714 7966 440967 7799 7991 6259976 7921 8048 8030 8051 8103 8118 8148 8130 8186 8892 8635 10430 8858 10722 10560 10882 10821 11002 10890 11124 10976 11509 12020 11552 12178 12232 12180 12741 12348 14286 12367 15380 17100 16666 18827 19310 22873 22201 24915 22386 26331 31244 31249 31276 31265 62336 61016 62433 62444 91497 62465 440917 93009 440967 637566 443158 5281168 5281515 5283349 6259976

Annex 1

TABLE A.1 Database of 1554 odorant components with CAS Numbers Acetal 105-57-7 Acetaldehdye phenethyl propyl acetal 7493-57-4 Acetaldehyde 75-07-0 Acetaldehyde ethyl cis-3-hexenyl acetal 28069-74-1 Acetaldehyde ethyl hexyl acetal 54484-73-0 Acetaldehyde ethyl isoeugenyl acetal 84029-92-5 Acetaldehyde ethyl linalyl acetal 40910-49-4 Acetaldehyde ethyl phenethyl acetal 2556-10-7 Acetaldehyde trans-3-hexenyl acetal 60763-40-8 Acetalpyrazine, 2-22047-25-2 Acetanisole 100-06-1 Acetate C-11 112-19-6 Acetate C-12 112-66-3 Acetate C-7 112-06-1 Acetate C-8 112-14-1 Acetate C-9 143-13-5 Acethoxy-3-ethoxybenzaldehyde, 4-72207-94-4 Acetic Acid 64-19-7 Acetoin 513-86-0 Acetone 67-64-1 Acetophenone 98-86-2 Acetovanillone 498-02-2 Acetoxy-1-ethynyl-2-sec-butylcyclohexane, 1-37172-05 Acetoxy-3-butanone, 2-4906-24-5 Acetoxy-3-ethoxybenzaldehyde, 4-72207-94-4 Acetoxy-3-pentyltetrahydropyran, 4-18871-14-2 Acetoxydihydrotheaspirane, 6-57893-27-3 Acetoxyphenyl)-2-butanone, 4-(p-3572-06-3 Acetyl carene 3608-11-5 Acetyl cedrene 32388-55-9 Acetyl isovaleryl 13706-86-0 Acetyl propionyl 600-14-6 ACETYL VALERYL 96-04-8 Acetyl-1,1,2,3,3,6-hexamethylindan, 5-15323-35-0 Acetyl-1-methylpyrrole, 2-932-16-1 Acetyl-2,5-dimethylthiophene, 3-2530-10-1 Acetyl-2-5dimethylfuran, 3-10599-70-9 Acetyl-2-thiazoline, 2-29926-41-8 Acetyl-3,5(or 6)-dimethylpyrazine, 2-54300-08-2 Acetyl-3-ethylprazine, 2-32974-92-8 Acetyl-3-isopropyl-1,1,2,6-tetramethylindane 68140-48-7 Acetyl-3-methylpyrazine, 2-23787-80-6 Acetyl-5-methylfuran, 2-1193-79-9 Acetyl-6-t-butyl-1, 1-dimethylindan, 4-13171-00-1 Acetylbutyryl 3848-24-6 Acetylpyridine, 2-1122-62-9 Acetylpyridine, 3-350-03-8 Acetylthiazole, 2-24295-03-2 Acetylthiophene 88-15-3 Adipic acid 124-04-9 AAET 88-29-9 Alanine, B-107-95-9 Alanine, dl-302-72-7 Alanroot oil 97676-35-2 Alcohol C-6 111-27-3 Alcohol C-7 111-70-6 Alcohol C-9 143-08-8 Alcohol C-11 Undeyclenic 112-43-6 Alcohol C-11 Undeyclic 112-42-5 Alcohol C-14 MYRISTIC 112-72-1 Aldehyde C-11, undecylenic 112-45-8 Aldehyde C-12 MNA 110-41-8 Aldehyde C-14, myristic 124-25-4 Aldehyde C-6 66-25-1 Aldehyde C-7 dimethyl acetal 10032-05-0 Allo-ocimenol 22450-63-4 Ally 2-ethylbutyrate 7493-69-8 Ally 2-furoate 4208-49-5 Allyl alpha-ionone 79-78-7 Allyl butyrate 2051-78-7 Allyl cinnamate 1866-31-5 Allyl cyclhexylacetate 4728-82-9 Allyl cyclohexanepropionate 2705-87-5 Allyl disulfide 2179-57-9 Allyl heptylate 142-19-8 Allyl hexanoate 123-68-2 Allyl isothiocyanate 57-06-7 Allyl isovalerate 2835-39-4 allyl mercaptan 870-23-5 Allyl nonanoate 7493-72-3 Allyl octanoate 4230-97-1 Allyl phenoxyacetate 7493-74-5 Allyl phenylacetate 1797-74-6 Allyl propionate 2408-20-0 Allyl sulfide 592-88-1 Allyl thiopropionate 41820-22-8 Allyl tiglate 7493-71-2 Allyl trimethyl hexanoate 71500-37-3 Allyl-2,6-diemthoxyphenol, 4-6627-88-9 Allylanisole, 4-140-67-0 Almond oil, bitter 8013-76-1 Almond oil, sweet 8007-69-0 Amberette seed (n) 8015-62-1 Ambergris tincture 8038-65-1 Ambrettolide 7779-50-2 Ambroxide, (-)-6790-58-5 Aminoacetophenone, 2-551-93-9 Ammonium sulfide 12135-76-1 Amyl (iso) benzoate 94-46-2 Amyl (iso) cinnamate 7779-65-9 Amyl 2-furoate 1334-82-3 Amyl acetate 628-63-7 Amyl alcohol 71-41-0 Amyl cinnamic aldehyde diethyl acetal, “alpha-60763-41-9 Amyl cinnamic aldehyde, alpha-122-40-7 Amyl cinnamylidene/methyl anthranilate Schiff base, alpha-68527-78-6 Amyl formate 638-49-3 Amyl hexanoate 540-07-6 Amyl isoeugenol 10484-36-3 AMYL ISOVALERATE 25415-62-7 Amyl octanoate 638-25-5 Amyl vinyl carbinyl acetate 2442-10-6 Amyl(iso) salicylate 87-20-7 Amyl-alpha-pyrone, 6-27593-23-3 Amylcinnamaldehyde dimethyl acetal, alpha-91-87-2 Amylcinnamic aldehyde dimethyl acetal, alpha-91-87-2 Amylcinnamyl acetate, alpha 7493-78-9 Amylcinnamyl alcohol, alpha 101-85-9 Amylcyclohexanone, 4-t-16587-71-6 Amylcyclohexyl acetate (mixed isomers) 67874-72-0 Amylcyclopentenone 25564-22-1 Amylvinylcarbinol 3391-86-4 Anethole 4180-23-8 Angelica oil (root or seed) 8015-64-3 Angelicalactone, alpha 591-12-8 Anisaldehyde, o-135-02-4 Anisaldehyde, p-123-11-5 Anise oil 84775-42-8 Anisic acid, p-100-09-4 Anisole 100-66-3 Anisyl acetate 104-21-2 Anisyl alcohol 105-13-5 Anisyl formate 122-90-8 Anisyl n-butyrate 6963-56-0 Anisyl phenylacetate 102-17-0 Anisyl propionate 7549-33-9 Anisylidene acetone 943-88-4 Arabinogalactan 9036-66-2 Armoise oil 8008-93-3 Artemisia oil 8008-93-3 Azodicarbonamide 123-77-3 Baccartol 68916-62-1 Balsam, Canadian 8021-28-1 Balsam, Peru 8007-00-9 Basil oil, sweet 8015-73-4 Bay oil 8006-78-8 Bay oil, sweet 8007-48-5 Beeswax absolute 8012-89-3 Benezethiol 108-98-5 Benzaldehyde 100-52-7 Benzaldehyde dimethyl acetal 1125-88-8 Benzaldehyde glyceryl acetal 1313-88-6 Benzaldehyde propylene glycol acetal 2568-25-4 Benzenethiol 108-98-5 Benzhydrol 91-01-0 Benzoic acid 65-85-0 Benzoin 579-44-2 Benzoin (resinoid) 9000-05-9 Benzonitrile 100-47-0 Benzophenone 119-61-9 Benzothiazole 95-16-9 Benzyl acetate 140-11-4 Benzyl acetoacetate 5396-89-4 Benzyl acetone 2550-26-7 Benzyl alcohol 100-51-6 Benzyl benzoate 120-51-4 Benzyl butyrate 103-37-7 Benzyl cinnamate 103-41-3 Benzyl disulfide 105-60-7 Benzyl formate 104-57-4 Benzyl isoamyl ether 122-73-6 Benzyl isobutyrate 103-28-6 Benzyl isoeugenol 120-11-6 Benzyl isovalerate 103-38-8 Benzyl laurate 140-25-0 Benzyl mercaptan 100-53-8 Benzyl phenylacetate 102-16-9 Benzyl propionate 122-63-4 Benzyl salicylate 118-58-1 Benzyl trans-2-methyl-2-butenoate 37526-88-8 BENZYL TRANS-2-METHYL-2-BUTENOATE 37526-88-8 Benzylidene acetone 122-57-6 Bergamot oil 8007-75-8 Beta ionone 14901-07-6 BETA-IONONE 1490-07-6 BETA-PINENE 18172-67-3 Biphenyl 92-52-4 Birch tar oil 8001-88-5 Bis(methylthio)methane 1618-26-4 Bisabolene 495-62-5 Bitter orange oil 68916-04-1 Bois de rose oil, acetylated 68952-69-2 Bois de rose oil, Brazilian 8015-77-8 Boldo leaf oil 8022-81-9 Borneol acetate, laevo-5655-61-8 Borneol, 1-464-45-9 Bornyl isovalerate 76-50-6 Bromstyrol 103-64-0 Butanedithiol, 1,3-24330-52-7 Butanedithiol, 2,3-4532-64-3 Butanethiol, 1-109-79-5 Butanethiol, 2-513-53-1 BUTYL 2-METHYLBUTYRATE 15706-73-7 Butyl acetate 123-86-4 Butyl alcohol 71-36-3 Butyl anthranilate, n-7756-96-9 Butyl benzoate 136-60-7 Butyl butyrolactate 7492-70-8 Butyl caproate 626-82-7 Butyl cinnamate aldehyde 1492-44-6 Butyl cinnamate, n-538-65-8 Butyl formate 596-84-7 BUTYL HEPTANOATE 5454-28-4 Butyl isobutyrate 97-87-0 Butyl isovalerate, n-109-19-3 Butyl lactate 138-22-7 Butyl laevulinate 2052-15-5 Butyl laurate 106-18-3 Butyl n-butyrate 109-21-7 Butyl oleate 142-77-8 Butyl phenylacetate 122-43-0 Butyl phthalide, 3-6066-49-5 Butyl p-hydroxybenzoate 94-26-8 Butyl propionate, n-590-01-2 Butyl salicylate, n-2052-14-4 Butyl stearate 123-95-5 Butyl sulfide 554-40-1 Butyl undecylenate 109-42-2 BUTYL VALERATE 591-68-4 Butyl-2, 4-dimethyldihydropyrane, 6-24237-00-1 Butyl-4, 4, 6-trimethyl-1, 3-dioxane, 2-54546-26-8 Butyl-alpha-methylhydrocinnamic aldehyde 80-54-6 Butyl-alpha-methylhydrocinnamic aldehyde 80-54-6 Butylamine 109-73-9 Butylated hydroxytolulene 128-37-0 Butylcyclohexanol, 4-tert-98-52-2 Butylcyclohexanone, 2-sec-14765-30-1 Butylcyclohexanone, p-tert-98-53-3 Butylcyclohexyl acetate, 2-t-88-41-5 Butylcyclohexyl acetate, 4-tert-32210-23-4 Butyldihydrocinnamaldehyde, p-tert-18127-01-0 Butylidene phthalide, 3-551-08-6 Butyl-1-lactate 34451-19-9 Butylphenol, p-tert-98-54-4 Butylquinoline, sec-133-58-0 Butyraldehyde, n-123-72-8 Butyric acid, n-107-92-6 Butyrophenone 495-40-9 Cabreuva oil 68188-03-4 Cade oil rectified 8013-10-3 Cadinene 29350-73-0 Caffeine 58-08-2 Cajeput oil 8008-98-8 Calamus oil 8015-79-0 Camphene 5794-03-6 Camphor oil 8008-51-3 Camphor, USP 464-49-3 Cananga oil 68606-83-7 Caprylic acid 124-07-2 Caraway oil 8000-42-8 Cardamom seed oil 8000-66-6 Carene, delta-3-13466-78-9 Carrot oil 8015-88-1 Carvacrol 499-75-2 CARVACRYL ETHYL ETHER 4732-13-2 CARVACRYL METHYL ETHER 6379-73-3 CARVEOL 99-48-9 Carveol, laevo-2102-59-2 Carvomenthenol. 4-562-74-3 Carvone,-1 99-48-9 Carvone, delta 2244-16-8 Carvone, laevo 6485-40-1 CARVYL ACETATE 97-42-7 Carvyl propionate 97-45-0 Carvylacetate, laevo-1205-42-1 Caryophyllene acetate 32214-91-8 Caryophyllene alcohol 4586-22-5 Caryophyllene oxide 1139-30-6 CARYOPHYLLENE, BETA-87-44-5 Cascarilla oil 8007-06-5 Cassia bark oil 8007-80-5 Castor oil 8001-79-4 Castoreum 8023-83-4 Cedar leaf oil 8007-20-3 Cedarwood oil atlas 8023-85-6 Cedarwood oil, Texas 68990-83-0 Cedarwood oil, Virginia 8000-27-9 Cedr-8-ene epoxide 13567-39-0 Cedrane, alpha 469-61-4 CEDRENOL 28231-03-0 Cedrenyl acetate 1405-92-1 CEDROL 77-53-2 CEDROL METHYL ETHER 19870-74-7 Cedryl acetate 77-54-3 Cedryl formate 39900-38-4 Celery seed oil 8015-90-5 Cetyl acetate 629-70-9 Cetyl alcohol 36653-82-4 Chamomile flower, Hungarian, oil 8002-66-2 Chamomile oil, Roman 8015-92-7 Chenopodium oil 8006-99-3 CINEOLE, 1,4-470-67-7 CINEOLE, 1,8-470-82-6 CINNAMALDEHYDE 104-55-2 CINNAMIC ACID 621-82-9 Cinnamic alcohol 104-54-1 Cinnamic aldehyde dimethyl acetal 4364-06-1 Cinnamon bark oil. “Caylon” 8007-80-5 CINNAMYL ACETATE 103-54-8 Cinnamyl anthranilate 87-29-6 Cinnamyl benzoate 5320-75-2 CINNAMYL BUTYRATE 103-61-7 CINNAMYL BUTYRATE, trans 78761-39-4 CINNAMYL CINNAMATE 122-69-0 CINNAMYL FORMATE 104-65-4 CINNAMYL ISOBUTYRATE 103-59-3 CINNAMYL ISOVALERATE 104-27-2 CINNAMYL ISOVALERATE, trans 69121-78-4 CINNAMYL NITRILE 1885-38-7 CINNAMYL PROPIONATE 103-56-0 CINNAMYL PROPIONATE, trans 78761-38-3 Cinnamyl tiglate 61792-12-9 Cinnimic aldehyde methyl anthranilate 94386-48-8 Citral 5392-66-2 CITRAL DIETHYL ACETAL 7549-66-2 Citral dimethyl acetal 7549-37-3 Citral ethylene glycol acetal 66408-78-4 Citral-methlanthranilate (Schiff base) 67801-47-2 CITRIC ACID 77-29-9 Citronella oil 8000-29-1 Citronellal 106-23-0 Citronellic acid 502-47-6 Citronellol 106-22-9 Citronellol, (S)-(-)-B-7540-51-4 Citronellyl acetate 150-84-5 CITRONELLYL BUTYRATE 141-16-2 Citronellyl crotonate 68039-38-3 CITRONELLYL ETHYL OXALATE SUPRA 60788-25-2 Citronellyl ethyl ether 69929-16-4 CITRONELLYL FORMATE 105-85-1 CITRONELLYL ISOBUTYRATE 97-89-2 CITRONELLYL NITRILE 51566-62-2 Citronellyl oxyacetaldehyde 7492-67-3 Citronellyl phenylacetate 139-70-8 Citronellyl propanoate 141-14-0 CITRONELLYL TIGLATE 24717-85-9 Citronellyl valerate 7540-53-6 CITRONELLYL VALERATE 7540-53-6 CITRONELLYLOXY ACETALDEHYDE 50% BA 7492-67-3 Civet absolute 68916-26-7 Civetone 542-46-1 Clary oil 8016-63-5 Clove bud oil 8000-34-8 Cognac oil 8016-21-5 Copaiba oil 8001-61-4 Coriander oil 8008-52-4 Cornmint oil 68917-18-0 Costus root oil 8023-88-9 Coumarin 91-64-5 Cresol, m-108-39-4 Cresol, o-95-48-7 CRESOL, p-106-44-5 Cresyl benzoate, p-614-34-6 CRESYL CAPRYLATE, PARA-59558-23-5 Cresyl isobutyrate 103-93-5 CRESYL METHYL ETHER, P-104-93-8 Cresyl salicylate, p-607-88-5 Cubeb oil 8007-87-2 Cumin oil 8014-13-9 CUMINALDEHYDE 122-03-2 Cuminyl nitrile 13816-33-6 Curcuma oil 8024-37-1 Cyclamen alcohol 4759-19-8 Cyclamen aldehyde-methyl anthranilate 91-50-9 Cyclocitral, beta 432-25-7 Cyclohexancarboxyclic acid 98-89-5 Cyclohexaneacetic acid 5292-21-7 Cyclohexaneethyl acetate 21722-83-8 Cyclohexanol 108-93-0 Cyclohexyl acetate 622-45-7 CYCLOHEXYL BUTYRATE 1551-44-6 Cyclohexyl cyclohexanone, 2-90-42-6 CYCLOHEXYL ISOVALERATE 7774-44-9 CYCLOHEXYL PROPIONATE 6222-35-1 Cyclohexylethyl alcohol 4442-79-9 Cyclopentadecanolide 106-02-5 Cyclopentadecanone 502-72-7 Cyclopentanethiol 1679-07-8 Cyclopentanone 120-92-3 Cyclopentenyl proionate musk 84012-64-6 CYMENE, PARA-99-87-6 Cypress oil 8013-26-0 Cyste absolute 8016-26-0 Cysteine, L-52-90-4 Davana oil 8016-03-3 Decadienal, trans, trans-2,4-25152-84-5 Decadienal, 2,4-2362-88-4 DECADIENAL, trans, trans-2,4-25152-84-5 Decahydro-2-naphthol 825-51-4 Decahydro-beta-naphthyl acetate 10519-11-6 Decahydro-beta-naphthyl formate 10519-12-7 Decalactone, 5579-78-2 DECALACTONE, DELTA-705-86-2 DECALACTONE, GAMMA-706-14-9 DECANAL 112-31-2 DECANAL DIMETHYL ACETAL 7779-41-1 Decanal, trans-2-3913-71-1 Decanal, cis-4-21662-09-9 DECANOIC ACID 334-48-5 DECANOL, 1-112-30-1 Decanone, 2-693-54-9 Decanone, 3-928-80-3 DECEN-2-ONE, 3-10519-33-2 Decenal, trans-2-3913-71-1 Decenoic acid, 9-14436-32-9 Decenyl acetate, 9-50816-18-7 Decenyl acetate, 9-50816-18-7 DECYL ACETATE 112-17-4 DECYL BUTYRATE 5454-09-1 Decyl methyl ether 7289-52-3 DECYL PROPIONATE 5454-19-3 Decyl vinyl ether 765-05-9 Decylenic alcohol 13019-22-2 Dedecenal, 2-4826-62-4 Deertongue absolute 68606-82-6 Deobase 8020-83-5 DIACETYL 431-03-8 Dibenzyl 103-29-7 Dibenzyl ether 103-50-4 Dibutyl sulfide 544-40-1 Dicyclohexyl disulfide 2550-40-5 Diethyl ketone 96-22-0 Diethyl L-tartrate 87-91-2 Diethyl malate 626-11-9 Diethyl malonate 105-53-3 Diethyl malonate 141-05-9 Diethyl sebacate 110-40-7 Diethyl succinate 123-25-1 Diethyl-5-methylpyrazine 18138-04-0 Diethylene glycol monoethyl ether 111-90-0 Diethylene glycol monomethyl ether 111-77-3 Diethylpyrazine, 2,3 15707-24-1 Difuran, 2,2′-(Thiodimethylene) 1678-67-6 Dihexyl furmarate 19139-31-2 DIHYDRO CARVYL ACETATE, 1-20777-49-5 Dihydro-2,4,6-trimethyl-1,3,5(4H)-dithiazine 94944-51-1 Dihydro-3(2H)thiophenone, 4,5-1003-04-9 DIHYDRO-ALPHA-IONON 31499-72-6 Dihydro-alpha-terpineol 498-81-7 DIHYDROANETHOLE 140-45-0 Dihydrocarveol 619-01-2 Dihydrocarveol 7764-50-3 DIHYDROCARVONE 5524-05-0 DIHYDROCOUMARIN 119-84-6 Dihydroeugenol 2785-87-7 Dihydrogeraniol, 6,7-40607-48-5 Dihydroisojasmone 95-41-0 DIHYDROJASMONE 1128-08-1 Dihydromethyl-alpha-ionone 68480-17-1 Dihydromyrcene 2436-90-0 DIHYDROMYRCENOL 53219-21-9 DIHYDROMYRCENYL ACETATE 53767-93-4 DIHYDRONOOTKATONE 20489-53-6 Dihydrosafrole 94-58-6 Dihydroterpinyl acetate 80-25-1 Dihydroxy-3-methylbenzaldehyde 6248-20-0 Dihydroxybenzaldehyde, 3,4-139-85-5 Diisopropropylphenol 2078-54-8 Dill oil 8006-75-5 Dill seed oil. Indian 8016-06-6 Dimethoxyacetophenone, 3,4-1131-62-0 Dimethoxybenzene, m-151-10-0 Dimethoxyethane 1,1-534-15-6 DIMETHOXYPHENOL, 2,6-91-10-1 DIMETHYL ANTHRANILATE 85-91-6 DIMETHYL BENZALDEHYDE, 2,4-15764-16-6 DIMETHYL BENZYL CARBINYL BUTYRATE 10094-34-5 Dimethyl carbonate 616-38-6 Dimethyl citraconate 617-54-9 Dimethyl disulfide 624-92-0 Dimethyl malonate 108-59-8 DIMETHYL OCTENYL ACETATE 102-58-9 DIMETHYL PHENYL CARBINOL 617-94-7 DIMETHYL PHENYL ETHYL CARBINOL 103-07-1 Dimethyl succinate 106-65-0 Dimethyl sulfide 75-18-3 Dimethyl trisulfate 3658-80-8 DIMETHYL-1,2-CYCLOPENTADIONE, 3,4-13494-06-9 DIMETHYL-1-OCTANOL, 3,7-106-21-18 Dimethyl-1-octanol, 3,7-106-21-8 Dimethyl-1-octanyl butyrate, 3,7-67874-80-0 Dimethyl-2,4,6--octatriene, 2,6-3016-19-1 Dimethyl-2,6-nonadienenitrile, 3,7-61792-11-8 Dimethyl-2-heptanol, 2,6-13254-34-7 Dimethyl-3.5.9-undecatrien-2-one, 6,10-141-10-6 DIMETHYL-3-HYDROXY-2,5-DIHYDROFURAN-2-ONE, 4,5-28664-35-9 Dimethyl-3-octanol, 3,6-151-19-9 Dimethyl-3-octanyl acetate 60763-42-0 Dimethyl-4,6,10-dodecatrien-3-one, 7,11-26651-96-7 Dimethyl-4-heptanol, 2,6-108-82-7 Dimethyl-4-heptanone, 2,6-108-83-7 Dimethyl-4-methoxy-3(2H)-furanone, 2,5-4077-47-8 Dimethyl-5-acetylthiazole, 2′,4′-89-74-7 DIMETHYL-5-HEPTEN-1-AL, 2,6-106-72-9 Dimethyl-7-methoxyoctan-2-ol, 3,7-41890-92-0 Dimethyl-8-tert-butylcoumarin, 4,6-17874-34-9 Dimethyl-9-undecen-2-one, 6,10-4433-36-7 Dimethylacetophenone, 2,4 89-74-7 Dimethylbenzyl acetate, 2,4-632346-96-7 Dimethylbenzylcarbinyl propionate 67785-77-7 Dimethylfuran, 2,5-625-86-5 Dimethylhydroquinone 150-78-7 Dimethylionone 68459-99-4 Dimethyloctanyl acetate, 3,7-2078-49-8 Dimethyl-p-ethylphenylpropanal, alpha, alpha-2661-96-7 Dimethylphenethyl acetate, alpha, alpha-151-05-3 Dimethylphenethyl alcohol, alpha, alpha 100-86-7 Dimethylpyrazine, 2,3-5910-89-4 Dimethylpyrazine, 2,6-108-50-9 Dimethylpyridine, 2,6-108-48-5 Dimethyltetrahydrobenzaldehyde 68737-61-1 Dimethylthiazole, 4,5-3581-91-7 Dimethylthiophenol, 2,6-118-72-9 Dimyrcetal 18479-58-8 Dipentene 138-86-3 Diphenyl ether 101-84-8 Diphenyl-2-propanone, 1,3-102-04-5 Diphenylamine 122-39-4 Diphenylmethane 101-81-5 Dipropylene glycol 25265-71-8 Disodium succinate 150-90-3 dl-MENTHYL ACETATE 16409-45-3 DODECADIENAL, trans,trans-2,4-21662-16-8 Dodecalactone, delta-713-95-1 Dodecalactone, gamma-2305-05-7 DODECENAL, trans-2-4826-62-4 Eau de brouts absolute 8030-28-2 Elemi oil 8023-89-0 Eposxyguaiene 68071-23-8 Estragon oil 8016-88-4 Ethanedithiol, 1,2-540-63-6 Ethoxy-3,7-dimethyloctanal, 7-3613-33-0 Ethoxybenzaldehyde, p-10031-82-0 Ethoxythiazole, 2-15679-19-3 ETHYL 10-UNDECENOATE 692-86-4 Ethyl 2-mercaptopropionate 19788-49-9 Ethyl 2-methoxybenzyl ether 64988-06-3 Ethyl 2-methyl-4-pentenoate 53399-81-8 Ethyl 2-methylbutyrate 7452-79-1 Ethyl 2-methylpentanoate 28959-02-6 Ethyl 3-(2-furyl)propanoate 94278-27-0 Ethyl 3-(methylthio)propionate 13327-56-5 Ethyl 3-hydrohexanoate 2305-25-1 ETHYL 3-HYDROXY-BUTYRATE 5405-41-4 Ethyl 3-hyroxybutyrate 5405-41-4 ETHYL 3-METHYLTHIO-PROPIONATE 13327-56-5 Ethyl 3-phenylglycidate 121-39-1 Ethyl 3-phenylpropionate 2021-28-5 ETHYL ACETATE 141-78-6 ETHYL ACETOACETATE 141-97-9 Ethyl acrylate 140-88-5 ETHYL AMYL KETONE 106-68-3 ETHYL ANISATE 94-30-4 Ethyl anthranilate 87-25-2 ETHYL BENZOATE 93-89-0 Ethyl benzoylacetate 94-02-0 ETHYL BUTYL KETONE 106-35-4 ETHYL BUTYRATE 105-54-4 Ethyl caprylate 106-32-1 ETHYL CINNAMATE 103-36-6 Ethyl citral 41448-29-7 ETHYL CROTONATE 10544-63-5 Ethyl cyclohexanepropionate 10094-36-7 ETHYL DECANOATE 110-38-3 ETHYL FORMATE 109-94-4 ETHYL HEPTOATE 106-30-9 ETHYL HEXANOATE 123-66-0 Ethyl hexyl salicylate 118-60-5 ETHYL ISOBUTYRATE 97-62-1 ETHYL ISOVALERATE 108-64-5 ETHYL LACTATE 97-64-3 ETHYL LAURATE 106-33-2 ETHYL LEVULINATE 539-88-8 Ethyl linalool 10339-55-6 Ethyl linalyl acetate 61931-80-4 Ethyl maltol 4940-11-8 Ethyl methylphenylglycidate 77-83-8 ETHYL MYRISTATE 124-06-1 Ethyl myristate 124-06-1 ETHYL OCTANOATE 106-32-1 Ethyl octine carbonate 10031-92-2 Ethyl oleate 111-62-6 ETHYL PALMITATE 628-97-7 ETHYL PELARGONATE 123-29-5 Ethyl phenylacetate 101-97-3 ETHYL PROPIONATE 105-37-3 Ethyl pyruvate 617-35-6 ETHYL SALICYLATE 118-61-6 Ethyl sorbate 2396-84-1 Ethyl stearate 111-61-5 Ethyl thioacetate 625-60-5 ETHYL TIGLATE 5837-78-5 ETHYL TRANS-2,CIS-4-DECADIENOATE 3025-30-7 Ethyl trans-2-decanoate 7367-88-6 Ethyl trans-2-octenoate 2356-90-8 Ethyl trans-3-hexenoate 2396-83-0 Ethyl undecanoate 627-90-7 ETHYL UNDECYLENATE 692-86-4 ETHYL VALERATE 539-82-2 Ethyl valerate 539-82-2 ETHYL VANILLIN 121-32-4 ETHYL VANILLIN PROPYLENE GLYCOL ACETAL 68527-76-4 Ethyl vinyl ketone 1629-58-9 Ethyl(p-tolyloxy)acetate 67028-40-4 Ethyl-2-hydroxy-2-cyclpenten-1-one, 3-21835-01-8 ETHYL-2-METHYL-4-PENTENOATE 53399-81-8 ETHYL-2-METHYLBUTYRATE 7452-79-1 Ethyl-2-methylpyrazine, 3-15707-23-0 Ethyl-2-methylpyridine, 5-104-90-5 Ethyl-3,5(or 6)-dimethylpyrazine, 2-55031-15-7 Ethyl-3-hydroxy-4-methyl-2(5H)-furanone 698-10-2 Ethylacetoacetate ethylene glycol ketal 6413-10-1 Ethylbenzaldehyde, 4-4748-78-1 Ethylbenzene 100-41-4 Ethylbutyl acetate, 2-10031-87-5 Ethylbutyraldehyde, 2-97-96-1 Ethylbutyric acid, 2-88-09-5 Ethylcellulose 9004-57-3 Ethylene brassylate 105-95-3 Ethylene dodecanedioate 54982-83-1 Ethylfenchol, 2-18368-91-7 Ethylhexanal 123-05-7 Ethylhexanol, 2-104-76-7 Ethylhexyl acetate 103-09-3 Ethylphenol, p-123-07-9 Ethylpyrazine, 2-13925-00-3 Ethylquaiacol, 4-2785-89-9 Ethylthiophenol, 2-4500-58-7 Eucalptus citriodora oil, acetylated 68991-29-7 Eucalyptus citriodora oil 8000-48-4 Eugenol 97-53-0 EUGENYL ACETATE 93-28-7 Eugenyl formate 10031-96-6 Eugenyl methyl ether 93-15-2 Eugenyl phenylacetate 10402-33-2 Farnesol 4602-84-0 FENCHYL ACETATE 13851-11-1 FENCHYL ALCOHOL, ALPHA-1632-73-1 Fennel oil 8006-84-6 Fenugreek absolute 84625-40-1 Fig leaf absolute 68916-52-9 Fir Balsam, Oregan 8021-28-1 Fir needle oil 8021-28-1 Flouve oil 68916-09-6 Formaldehyde cyclodecyl ethyl acetal 58567-11-6 Formaldehyde cyclododecyl methyl acetal 42604-12-6 Formic acid 64-18-6 Formyl-1-methyl-4-(4-methyl-pentyl)-3-cyclohexene 66327-54 Formyl-6,6-dimethylbicyclo(3.1.1)hept-2-ene, 2-564-94-3 Formylethyltetramethytetralin 58243-85-9 Frankincense gum 8050-07-5 Frenchone 1195-79-5 Fumaric acid 110-17-8 Furanmethanethiol formate, 2-59020-90-5 FURFURAL 98-01-1 Furfuryl 3-methylbutanoate 13678-60-9 Furfuryl acetate 623-17-6 Furfuryl alcohol 98-00-0 FURFURYL BUTYRATE 623-21-2 FURFURYL HEXANOATE 39252-02-3 Furfuryl isopropyl sulfide 1883-78-9 Furfuryl mercaptan 98-02-2 Furfuryl methyl sulfide 1438-91-1 Furfuryl octanoate 39252-03-4 Furfuryl pentanoate 36701-01-6 Furfuryl propionate 623-19-8 Furfuryl thioacetate 13678-68-7 Furfuryl thiopropionate 59020-85-8 Furfurylpyrrole 1438-94-4 Furyl methyl ketone, 2-1192-62-7 Furyl)acrolein, 3-(2-623-30-3 Galbanum oil 8023-91-4 Galbanum resin 9000-24-2 Genet absolute 90131-21-8 Geraniol 106-24-1 Geranium oil 8000-46-2 GERANYL 2-METHYL BUTANOATE 68705-63-5 GERANYL ACETATE 105-87-3 GERANYL ACETOACETATE 10032-00-5 Geranyl acetone 3796-70-1 Geranyl benzoate 94-48-4 GERANYL BUTYRATE 106-29-5 Geranyl crotonate 5617-46-4 Geranyl ethyl ether 40267-72-9 Geranyl formate 105-86-2 GERANYL ISOBUTYRATE 2345-26-8 Geranyl isovalerate 109-20-6 Geranyl linalool 1113-21-9 Geranyl nitrile 5146-66-7 Geranyl oxyacetaldehyde 65405-73-4 Geranyl phenylacetate 102-22-7 GERANYL PROPIONATE 105-90-8 Geranyl tiglate 7785-33-3 Ginger oil 8007-08-7 Glucose pentaacetate 604-68-2 Glutamic acid, 1-56-86-0 Glycerol 56-81-5 Glycine 56-40-6 Grapefruit oil 8016-20-4 Guaiac wood oil 8016-23-7 GUAIACOL 90-05-1 Guaicwood acetate 61789-17-1 Guaiene 88-84-6 Gurjun oil 8030-55-5 Hay absolute 8031-00-3 Helichrysum oil 8023-95-8 Heliotropin 120-57-0 Heptadienal, 2,4-4313-03-5 HEPTADIENAL, trans,trans-2,4-4313-03-5 HEPTALACTONE, GAMMA-105-21-5 Heptanal 111-71-7 Heptanoic acid 111-14-8 Heptanol, 2-543-49-7 Heptanol, 3-589-82-2 Heptenal, cis-4-6728-31-0 Heptenal, trans-2-18829-55-5 Heptyl butyrate 5870-93-9 Heptyl cyclopentanone, 2-n-137-03-1 Heptyl formate 112-23-2 Heptylfuran, 2-3777-71-7 Heptyltetrahydrofuran 2435-16-7 HEXADECANOLIDE 109-29-5 HEXADIENAL, TRANS,TRANS-2,4-142-83-6 HEXADIENAL, trans,trans-2,4-142-83-6 Hexadienyl isobutyrate, 2,4-16491-24-0 Hexahydro-4,6,6,7,8,8-hexamethylcyclopenta-gamma-2-benzopyran, 1,3,4,6,7,8- 1222-05-5 Hexalactone, delta-823-22-3 HEXALACTONE, GAMMA-695-06-7 Hexanedione, 3,4-4437-51-8 HEXANEDIONE,3,4-4437-51-8 Hexanedithiol, 1,6-1191-43-1 HEXANOIC ACID 142-62-1 Hexanol, 3-623-37-0 Hexanone, 3-589-38-8 Hexen-1-ol, 4-928-92-7 Hexen-1-ol, trans-2-928-95-0 Hexen-1-ol, cis-3-928-96-1 Hexen-2-al 6728-26-3 Hexen-3-one, 4-2497-21-4 Hexenal diethyl acetal, trans-2-67746-30-9 Hexenal dimethyl acetal, trans-2-18318-83-7 HEXENAL, CIS-3-6789-806 HEXENAL, trans-3-69112-21-6 Hexenoic acid, trans-2-13419-69-7 HEXENOL, “TRANS-2-928-95-0 Hexenyl 2-methylbutanoate, cis-3-10094-41-4 Hexenyl 3-methylbutanoate, 3-35154-45-1 Hexenyl acetate, 2-2497-18-6 Hexenyl acetate, cis-3-3681-71-8 Hexenyl anthranilate, cis-3-65405-76-7 HEXENYL BENZOATE, CIS-3-25152-85-6 Hexenyl butyrate, cis-3-16491-36-4 HEXENYL BUTYRATE, CIS-3-16491-36-4 Hexenyl formate, cis-3-33467-73-1 Hexenyl hexanoate, cis-3-31501-11-8 HEXENYL ISOBUTYRATE, CIS-3-41519-23-7 Hexenyl oxacetaldehyde, cis-3-68133-79-2 HEXENYL PHENYL ACETATE, CIS-3-42436-07-7 Hexenyl phenylacetate, cis-3-42436-07-7 Hexenyl propionate, trans-2-53398-80-4 HEXENYL PROPIONATE, CIS-3-33467-74-2 Hexenyl salicylate, cis-3-65405-77-8 HEXENYL TIGLATE, CIS-3-67883-79-8 HEXENYL VALERATE, CIS-3-35852-46-1 Hexenyl) dihydro-5-methyl-2 (3H) furanone, 5-(cis-370851-61-5 Hexenylcyclopentanone 34687-46-2 Hexoxyacetaldehyde dimethyl acetal 17597-95-4 Hexyl 2-formate 39251-86-0 Hexyl 2-methylbutyrate 10032-15-2 HEXYL ACETATE 142-92-7 HEXYL BENZOATE 6879-88-4 HEXYL BUTYRATE 2639-63-6 HEXYL CAPROATE 6378-65-0 HEXYL CAPRYLATE 1117-55-1 HEXYL CINNAMALDEHYDE, ALPHA-101-86-0 Hexyl crotonate 19089-92-0 Hexyl ethyl acetoacetate, n-29214-60-6 Hexyl formate 629-33-4 HEXYL HEXANOATE 6378-65-0 HEXYL ISOBUTYRATE 2349-07-7 HEXYL ISOVALERATE 10032-13-0 Hexyl neopentanoate 5434-57-1 Hexyl phenylacetate 5421-17-0 Hexyl propionate 2445-76-3 Hexyl salicylate 6259-76-3 HEXYL TIGLATE 16930-96-4 Hexyl-2-decenal, 2-13893-39-5 Hexylclopentanone, 2-13074-65-2 Hexylene glycol 107-41-5 Hibawood oil 68917-43-1 Ho leaf oil 8022-91-1 HOMOPRENYL ISOBUTYRATE 80118-06-5 Honeysuckle absolute 8023-93-6 Hyacinth absolute 8023-94-7 Hydratropic acetate 10402-52-5 Hydratropic alcohol 1123-85-9 Hydratropic aldehyde dimethyl acetal 90-87-9 Hydroabietyl alcohol 13393-93-6 Hydroxy-2,5-dimethyl-3(2H)-furanone, 4-3658-77-3 Hydroxy-2-butanone, 1-5077-67-8 Hydroxy-4-methylpentyl)-3-cyclohexene-1carboxaldehyde, 3 and 4-(4-31906-04-4 Hydroxyacetophenone, 2′-118-93-4 Hydroxybutanoic acid lactone, 4-96-48-0 Hydroxycitronellal 107-75-5 Hydroxycitronellal dimethyl acetal 141-92-4 Hydroxycitronellal methyl anthranilate 89-43-0 Hydroxycitronellal-Indole 68527-79-7 Hydroxycitronellol 107-74-4 Hydroxydihydrotheaspirane, 6-65620-50-0 Hydroxyphenyl)-2-butanone, 4-(p-5471-51-2 Hyssop oil 8006-83-5 Immortelle absolute 8023-95-8 Indole 120-72-9 IONOL, ALPHA-25312-34-9 Ionol, beta 22029-76-1 Ionone alpha 127-41-3 Ionone, beta-14901-07-6 Ionyl acetate, alpha 52210-18-1 IRONE, ALPHA-79-69-6 Isoamyl 3-(2-furan)propionate 7779-67-1 Isoamyl acetate 123-92-2 Isoamyl acetoacetate 2308-18-1 Isoamyl alcohol 123-51-3 ISOAMYL BUTYRATE 106-27-4 Isoamyl caproate 2198-61-0 Isoamyl caprylate 2035-99-6 ISOAMYL FORMATE 110-45-2 Isoamyl geranate 68133-73-3 ISOAMYL HEXANOATE 2198-61-0 Isoamyl isobutyrate 2050-01-3 ISOAMYL ISOVALERATE 659-70-1 Isoamyl laurate 6309-51-9 Isoamyl nonanoate 7779-70-6 Isoamyl phenylacetate 102-19-2 ISOAMYL PROPIONATE 105-68-0 Isoamyl pyruvate 7779-72-8 ISOAMYL TIGLATE 41519-18-0 Isoascorbic acid, D-89-65-6 Isoborneol 124-76-5 Isobornyl acetate 125-12-2 Isobornyl formate 1200-67-5 Isobornyl methyl ether 5331-32-8 Isobornyl propionate 2756-56-1 Isobutyl acetate 110-19-0 Isobutyl benzoate 120-50-3 ISOBUTYL BUTYRATE 539-90-2 Isobutyl caproate 105-79-3 ISOBUTYL CINNAMATE 122-67-8 ISOBUTYL FORMATE 545-55-2 Isobutyl formate 542-55-2 Isobutyl furylpropionate 105-01-1 Isobutyl heptylate 7779-80-8 ISOBUTYL HEXANOATE 105-79-3 ISOBUTYL ISOBUTYRATE 97-85-8 Isobutyl isobutyrate 97-85-8 Isobutyl isovalerate 589-59-3 Isobutyl linlaool 56105-46-5 ISOBUTYL PHENYLACETATE 102-13-6 Isobutyl propionate 540-42-1 Isobutyl salicylate 87-19-4 ISOBUTYL TIGLATE 61692-84-0 Isobutyl trans-2-butenoate 589-66-2 Isobutyl-3-methoxypryazine, 2-24683-00-9 Isobutylthiazole, 2-18640-74-9 Isobutyraldehyde 78-84-2 Isobutyric acid 79-31-2 Isocamphenyl cyclohexanol 66068-84-6 Isocyclocitral 3335-66-6 Isoeugenenyl methyl ether 93-16-3 Isoeugenol 97-54-1 Isoeugenyl acetate 93-29-8 Isoeugenyl phenylacetate 120-24-1 Isohexenyl cyclohexenyl carboxaldehyde 37677-14-8 Isojasmone 11050-62-7 Isoleucine, DL-443-79-8 Isolongifolanone 23787-90-8 Isomenthone 491-07-6 Isopentylamine 107-85-7 Isophorone 78-59-1 Isopropenyl-5-methyl-4-hexen-1-ol, 2 498-16-8 Isopropyl 2-methylbutyrate 66576-71-4 Isopropyl acetate 108-21-4 Isopropyl alcohol 67-63-0 Isopropyl butyrate 638-11-9 Isopropyl cinnamate 4480-06-5 ISOPROPYL ISOBUTYRATE 617-50-5 Isopropyl isobutyrate 617-50-5 Isopropyl myristate 110-27-0 Isopropyl palmitate 142-90-6 Isopropyl phenylacetaldehyde, p-4395-92-0 Isopropyl phenylacetate 4861-85-2 Isopropyl quinoline. 135-79-5 Isopropyl tiglate 6284-46-4 ISOPROPYL TIGLATE 1733-25-1 Isopropyl-1-methyl-2-propenylbenzene, 4-14374-92-6 Isopropyl-2-(1H)-octahydronaphthalenone 34131-98-1 Isopropyl-2-decahydroanaphthalenol 34131-99-2 Isopropyl-4-methylthiazole, 2-15679-13-7 Isopropyl-5-methyl-2-hexene-1-al, 2-35158-25-9 Isopropyl-5-methyl-2-hexene-1-ol, 2-40853-53-0 Isopropyl-5-methyl-2-hexene-1-yl acetate, 2-40853-56-3 Isopropylbenzyl alcohol, p-536-60-7 Isopropylcyclohexanol, p-4621-04-9 Isopropylphenol, 2-88-69-7 Isoproylbenzyl acetate, p-59230-57-8 Isopulegol 89-79-2 Isopulegol acetate 57576-09-7 Isopulegol 7786-67-6 Isopulegyl acetate 57576-09-7 Isoquinoline 119-65-3 Isosafrole 120-58-1 Isovaleric acid 503-74-2 ISOVANILLIN 621-59-0 JASMIN ACETATE 18871-14-2 Jasmine absolute 84776-64-7 Jasmone, cis-488-10-8 Jonquil absolute 8023-75-4 Juniper oil 8012-91-7 Karo karounds absolute 68916-95-0 Labdanum oil 8016-26-0 Lactic acid 598-82-3 Lactoscatone 21280-29-5 Laevulinic acid 123-76-2 Lauric acid 143-07-7 Lauric aldehyde 112-54-9 Lauryl alcohol 112-53-8 Lavandin benzol absolute 91722-69-9 Lavandin oil 8022-15-9 Lavandulyl acetate 25905-14-0 Lavender absolute 97660-01-0 Lavender oil 8000-28-0 Lemon oil 8008-56-8 Lemon petitgrain oil 8008-56-8 Lemongrass oil 8007-02-1 Leucine, L-61-90-5 Lilial-methyl anthranilate 91-51-0 Lime oil 8008-26-2 Limonene, 1-5989-54-8 Limonene, d-5989-27-5 LINALLYL PROPIONATE 144,39-8 Linaloe wood oil 8006-86-8 Linalool 78-70-6 Linalool oxide 1356-19-1 Linalyl acetate 115-95-7 Linalyl anthranilate 7149-26-0 Linalyl butyrate 78-36-4 Linalyl cinnamate 78-37-5 Linalyl formate 115-99-1 Linalyl isobutyrate 78-35-3 Linalyl isovalerate 1118-27-0 Linalyl methyl ether 60763-44-2 Linalyl phenylacetate 7143-69-3 Linalyl propionate 144-39-8 Linoleic acid 60-33-3 Litsea cubeba oil 68855-99-2 Longifolene 475-20-7 Lovage oil 8016-31-7 Mace oil 8007-12-3 Maltol 118-71-8 MALTOL ISOBUTYRATE 65416-14-0 Maltyl isobutyrate 65416-14-0 Mandrin oil 8008-1-9 Marjoram oil, Spanish 8016-3-9 Marjoram oil, sweet 8015-01-8 Mastic absolute 61789-92-2 Mentha citrata oil 68917-15-7 Menthadiene-7-methyl formate 6868-20-5 Menthalactone 13341-72-5 Menthen-6-yl)-1-propanone, 1-(para-175-17-4 MENTHOL ISOVALERATE 16409-46-4 Menthol, 1-2216-51-5 Menthol, racemic 89-78-1 Menthone, racemic 89-82-5 Menthyl acetate 16409-45- Menthyl acetoacetate 59557-05-0 Menthyl lactate, 1-61897-98-6 Menthyl phenylacetate, 1-26171-78-8 Mercapto-3-butanol, 2-37887-04-0 Mercaptopinane, 2,3, and 10-23832-18-0 Mercaptopropionic acid 79-42-5 Methionine, dl-59-51-8 Methoxy dicyclopentadiene carboxaldehyde 8680-90-9 Methoxy-3-(1-methylpropyl)pyrazine, 2-24168-70-5 Methoxy-3(5 or 6)-isopropylpyrazine, 2-93905-0-4 Methoxy-3-methylpyrazine, 2-2847-30-5 Methoxy-4-methylphenol, 2-93-51-6 Methoxy-4-vinylphenol, 2-7786-61-0 Methoxybenzaldehyde, o-135-02-4 METHOXYBENZALDEHYDE, PARA-123-11-5 METHOXYCINNAMALDEHYDE, ORTHO-1054-74-1 Methoxycitronellal 3613-30-7 Methoxycoumarin, 7-531-59-9 Methoxyhydratropaldehyde, p-5462-06-6 Methoxyphenyl) butan-2-one, 4-(p-104-20-1 Methoxyphenylacetone 122-84-9 Methoxypyrazine, 2-3149-28-8 Methyl 2-furoate 611-13-2 METHYL 2-METHYL-BUTYRATE 868-57-5 Methyl 2-methylbutyrate 868-87-5 Methyl 2-methylpentanoate 2177-77-7 Methyl 2-thiofuroate 13679-61-3 Methyl 3-(methylthio)propionate 13532-18-8 Methyl 3-hydroxyhexanoate 21188-58-9 METHYL 3-METHYLTHIO-PROPIONATE 13532-18-8 Methyl 3-nonenoate 13481-87-3 Methyl 3-phenylpropionate 103-25-3 Methyl 4-methylvalerate 2412-80-8 Methyl abietate 68189-14-1 METHYL ACETATE 79-20-9 Methyl acetoacetate 79-20-9 METHYL ACETOPHENONE, P-122-00-9 METHYL AMYL KETONE 110-43-0 Methyl anisate 121-98-2 METHYL ANTHRANILATE 134-20-3 METHYL BENZOATE 93-58-3 Methyl benzyl carbinyl acetate 2114--2 METHYL BUTYRATE 623-42-7 METHYL CAPROATE 106-70-7 Methyl cinnamate 103-26-4 Methyl crotonate 623-43-8 Methyl cyclohexanecarboxylate 4630-82-4 METHYL CYCLOPENTENOLONE 80-71-7 METHYL DECALACTONE, GAMMA-7011-83-8 METHYL DECANOATE 110-42-9 METHYL DIHYDROJASMONATE 24851-98-7 Methyl ester of rosin 8050-15-5 Methyl ethyl ketone 78-93-3 METHYL FURFURAL, 5-620-02-0 Methyl furfuryl disulfide 57500-00-2 METHYL FUROATE 611-13-2 Methyl heptanoate 106-73-0 Methyl heptenone 110-93-0 Methyl heptine carbonate 111-12-6 Methyl hydratropaldehyde, p-99-72-9 Methyl isobutrate 547-63-7 METHYL ISOBUTYRATE 547-63-7 Methyl isopropyl ketone 563-80-4 METHYL ISOVALERATE 556-24-1 Methyl isovalerate 556-24-1 Methyl jasmonate 1211-29-6 Methyl laurate 111-82-0 Methyl linolenate 301-00-8 METHYL MYRISTATE 124-10-7 Methyl myristate 124-10-7 METHYL NAPHTHYL KETONE, ALPHA-941-98-0 Methyl naphtyl ketone, beta-93-08-3 Methyl nicotinate 93-60-7 Methyl N-methylanthranilate 85-91-6 Methyl nonanoate 1731-84-6 Methyl nonyl ketone 112-12-9 Methyl nonylenate 111-79-5 METHYL OCTANOATE 111-11-5 Methyl octine carbonate 111-80-8 Methyl octyl acetaldehyde 19009-56-4 Methyl o-methoxybenzoate 606-45-1 METHYL PENTANOIC ACID, 3-97-61-0 METHYL PHENYL ACETATE 101-41-7 METHYL PHENYL CARBINYL PROPIONATE 120-45-6 Methyl p-hydroxybenzoate 99-76-3 METHYL PROPIONATE 554-12-1 Methyl propyl disulfide 2179-60-4 Methyl p-tert-butylphenylacetate 3549-23-3 Methyl p-tolulate 99-75-2 METHYL QUINOLINE, 6-91-62-2 Methyl salicylate 119-36-8 Methyl sorbate 689-89-4 Methyl sulfide 75-18-3 Methyl thiobutyrate 2432-51-1 Methyl tiglate 6622-76-0 Methyl trans-2-octenoate 7367-81-9 METHYL TRANS-2-OCTENOATE 7367-81-9 Methyl undecylenate 5760-50-9 Methyl valerate 624-24-8 Methyl-1,2-cyclohexanedione, 3-3008-43-3 Methyl-1-octen-3-ol, 3-24089-00-7 Methyl-1-pentanol, 3-589-5-5 Methyl-1-phenyl-2-pentanone, 4-5349-62-2 Methyl-2(3)-nonenenitrile, 3-53153-66-5 Methyl-2,6-dimethoxyphenol, 4-6638-05-7 Methyl-2-butanol, d1-3-598-75-4 Methyl-2-butenal, 3-107-86-8 Methyl-2-butenal, trans-2-497-03-0 Methyl-2-butenyl salicylate, 3-68555-58-8 Methyl-2-cyclohexen-1-one, 3-1193-18-6 Methyl-2-cyclopenten-1-one, 3-2758-18-1 Methyl-2-fury)butanal, 3-(5-31704-80-0 Methyl-2-pentanone, 4-108-10-1 Methyl-2-pentenal, 2-623-36-9 METHYL-2-PENTENAL, trans-2-623-36-9 Methyl-2-pentenoic acid, 2-16957-70-3 METHYL-2-PENTENOIC ACID, trans-2-16957-70-3 Methyl-2-phenyl-2-hexenal, 5-21834-92-4 Methyl-2-phenyl-2-pentenal, 4-26643-91-4 Methyl-2-thiophenecarboxaldehyde, 5-13679-70-4 Methyl-3-(p-isopropylphenyl) propionaldehyde, 2-103-95-7 Methyl-3,(5 or 6)-ethoxypyrazine, 2-32737-14-7 Methyl-3-butyltetrahydropyran-4-yl acetate, 5-96-17-3 Methyl-3-furanthiol, 2-28588-74-1 Methyl-3-heptanone oxime, 5-22457-23-4 Methyl-3-penten-2-one, 4-141-79-7 METHYL-4-PENTENOIC ACID, 2-1575-74-2 Methyl-4-phenyl-2butano1, 2-103-05-9 METHYL-4-PROPYL-1,3-OXATHIANE, 2-67715-80-4 METHYL-5-(METHYLTHIO)-FURAN, 2-13678-59-6 METHYL-5-THIAZOLE ETHANOLACETATE, 4-656-53-1 Methyl-5-thiazoleethanol acetate, 4-656-53-1 Methyl-5-thiazoleethanol, 4-137-00-8 METHYL-5-THIAZOLE-ETHANOL, 4-137-00-8 Methyl-5-vinylthiazole, 4-1759-28-0 METHYL-5-VINYL-THIAZOLE, 4-1759-28-0 Methyl-6,7-dihydrocyclopenta(b)-pyrazine, 5H-5-23747-48-0 Methylanisalacetone, alpha-104-27-8 Methylanisole, 2-578-58-5 Methylbenzyl acetate, 4-2216-45-7 METHYLBENZYL ACETATE, ALPHA 93-92-5 Methylbenzyl butyrate, alpha-3460-44-4 Methylbiphenyl, 4-644-08-6 Methylbuty acetate, 2-53496-15-4 Methylbutyl 2-methylbutyrate, 2-2445-78-5 Methylbutyl isovalerate, 2-2445-77-4 Methylbutyraldehyde, 3-590-86-3 Methylbutyric acid 600-07-7 Methylcinnamic alcohol, alpha 1504-55-8 METHYLCINNAMIC ALDEHYDE, ALPHA-101-39-3 Methylcoumarin, 6-92-48-8 Methylcoumarin, 7-2445-8-2 Methylcrotonic, 3-541-47-9 Methylcyclododecyl methyl ether, 1-37514-30-0 Methylcyclooctyl carbonate 61699-38-5 Methylcyclopentadecanone, 3-541-91-3 Methylheptanoic acid, 2-1188-02-9 Methylheptenol 1335-09-7 Methylhexlacetaldehyde 7786-29-0 Methylionol, alpha-70172-00-8 Methylnaphthalene, 1-90-12-0 Methylnonanoic acid, 4-45019-28-1 Methyloctan-3-ol, 3-540-36-3 Methyloctanoic acid, 4-54947-74-9 Methylpentanal, 2-123-15-9 Methylpentanoic acid, 4-646-07-1 METHYLPHENOXY ACETALDEHYDE, p-67845-46-9 Methylpropyl)thiazole, 2-(1-18277-27-5 Methylpyrazine, (Methylthio)-67952-65-2 Methylpyrazine, 2-109-08-0 Methylquinoxaline, 5-13708-12-8 Methyltetrahydrofuran-3-one, 2-3188-00-9 Methyltetrahydrothiophen-3-one, 2-13679-85-1 Methylthiazole, 4-693-95-8 Methylthio)-1-hexanol, 3-(51755-66-9 Methylthio)butanal, 3-(16630-52-7 Methylthio)butanol, 4-(20582-85-8 METHYLTHIO-1-HEXANOL, 3-5155-66-9 Methylthio-2-butanone, 4-34047-39-7 Methylthio-4-methyl-2-pentanone, 4-23550-40-5 Mimosa absolute 93685-96-2 Musk amberette 83-66-9 Musk tibetene 145-39-1 Musk tonquin tincture 8001-04-5 Musk xylol 81-15-2 Mycernol 543-39-5 MYRCENE 123-35-3 Myrcenyl acetate 1118-39-4 Myristic acid 554-63-8 Myrrh absolute 8016-37-3 MYRTENOL 515-00-4 Myrtenyl acetate 1079-01-2 Myrtle oil 8008-46-6 Naphthalenethiol, 2-9160-1 Naphthyl ethyl ether, beta-93-18-5 Naphthyl isobutyl ether 2173-57-1 Naphthyl methyl ether, beta 93-04-9 NEOHESPERlDIN DIHYDROCHALCONE (NHDC) 20702-77-6 Neomenthol, d-2216-52-6 NEROL 106-25-2 Nerol oxide 1786-08-9 Neroli oil 8016-38-4 Nerolidol 7212-44-4 Nerolidyl acetate 56001-43-5 NERYL ACETATE 141-12-8 Neryl butyrate 999-40-6 Neryl formate 2142-94-1 NERYL ISOBUTYRATE 2345-24-6 Neryl isovalerate 3915-83-1 NERYL PROPIONATE 105-91-9 Nonadienal, 2,6-557-48-2 Nonadienal, trans, trans-2,4-5910-87-2 NONADIENAL, trans,trans-2,4-6750-03-4 Nonadienol, 2,6-7786-44-9 NONALACTONE, DELTA-3301-94-8 NONALACTONE, GAMMA-104-61-0 NONANAL 124-19-6 NONANEDIOL ACETATE, 1,3-1322-17-4 Nonanedithiol, 1,9-3489-28-9 Nonanol, 2-628-99-9 NONANONE, 2-821-55-6 Nonanone, 3-925-78-0 Nonatrien-2-one, 3,5,7-17609-32-4 Nonen-1-ol, cis-41453-56-9 Nonen-1-ol, cis-6-35854-86-5 Nonenal, 2-2463-53-8 NONENAL, cis-6-2277-19-2 NONENAL, trans-2-18829-56-6 NONENOL, cis-6-35854-86-5 Nonyn-1-al dimethylacetal, 2-13257-44-8 NOOTKATONE EX VALENCENE 46674-50-4 Nopol 128-50-7 Nopyl acetate 128-51-8 Nutmeg oil 8008-45-5 Oakmoss resinoid 9000-50-4 Ocimene 13877-91-3 OCIMENOL 5986-38-9 Ocimenol acetate 72214-23-4 Ocotea cymbarum oil 68917-09-9 Octadienal, 2,4-5577-44-6 OCTADIENAL, trans,trans-2,4-5577-44-6 Octahydrocoumarin 4430-31-3 Octahydrocoumarin 4430-31-3 OCTALACTONE, DELTA-698-76-0 OCTALACTONE, GAMMA-104-50-7 OCTANAL 124-13-0 Octanedithiol, 1,8 1191-62-4 OCTANOL, 1-111-87-5 OCTANOL, 3-598-98-0 OCTANONE, 2-111-13-7 Octen-1-ol, cis-5-64275-73-6 Octen-2-one, 3-1669-44-9 Octenal, trans-2-2363-89-5 OCTENAL, trans-2-2363-89-5 Octyl 2-furoate 39251-88-2 Octyl acetate, 3-4864-61-3 Octyl butyrate 110-39-4 Octyl formate 112-32-3 OCTYL ISOBUTYRATE 109-15-9 OCTYL ISOVALERATE 7786-58-5 Octyl isovalerate 7786-58-5 Octyl salicylate 6969-49-9 Oleic acid 112-80-1 Olibanum absolute 8016-36-2 Orange flower absolute 8030-28-2 Orange oil expressed 8008-57-9 Origanum oil 8007-11-2 Orris absolute 8002-73-1 Oxahexadecanolide, 10-1725-01-5 Oxahexadecanolide, 11-3391-83-1 OXAHEXADECANOLIDE, 12-6707-60-4 Oxoisophorone, 4-1125-21-9 Oxybutyric acid, 2-600-18-0 Palmarosa oil 8014-19-5 Palmitic acid 57-10-3 Parsley herb oil 8000-68-8 Patchouly oil 8014-09-3 PELARGONIC ACID 112-05-0 Pennyroyal oil 801-99-8 Pentamethyl-4,6-dinitroindane 116-66-5 Pentanol, 2-6032-29-7 Pentanone, 2-107-87-9 Penten-2-one, 3-625-33-2 Penten-3-ol, 1-616-25-1 Pentenal, trans-2-1576-87-0 Pentenoic acid, 4-591-80-0 Pentyl 2-furyl ketone 14360-50-0 Pentyl cyclpentanone propanone 40942-73-2 Pentylfuran, 2-3777-69-3 Pentylidene cyclohexanone 256677-40-1 Pentylpyridine, 2-2294-76-0 Pepper, black oil 8006-82-4 PERILLA ALCOHOL 536-59-4 Perilla oil 6812-21-8 PERILLALDEHYDE 2111-75-3 Peru balsam oil 8007-00-9 Petigrain bigarade oil 8014-17-3 PHELLANDRENE, alpha 99-83-2 Phenethyl 2-furoate 7149-32-8 Phenethyl hexanoate 6290-37-5 Phenethyl octanoate 5457-70-5 Phenethylamine 64-04-0 Phenol 108-95-2 PHENOXY ACETALDEHYDE 2120-70-9 Phenoxyacetic acid 122-59-8 Phenoxyethyl isobutyrate 103-60-6 Phenoxyethyl propionate, 2-23495-12-7 Phenthyl isobutyrate 103-48-0 PHENYL ACETALDEHYDE 122-78-1 Phenyl acetyl nitrile 140-29-4 Phenyl anisole, o-86-26-0 Phenyl disulfide 882-33-7 PHENYL ETHYL ALCOHOL 60-12-8 PHENYL ETHYL BENZOATE, 2-94-47-3 PHENYL ETHYL BUTYRATE, 2-103-52-6 PHENYL ETHYL CINNAMATE, 2-103-53-7 PHENYL ETHYL FORMATE, 2-104-62-1 PHENYL PROPYL ALDEHYDE 122-97-4 Phenyl salicylate 118-55-8 Phenyl-1,2-propanedione, 1-579-07-7 Phenyl-1-pentanol, 5-10521-91-2 Phenyl-1-propanol, 1-93-54-9 Phenyl-2-pentanol, 1-705-73-7 Phenylacetaldehyde 2,4-dihydroxy-2-methylpentane acetal 67633-94-7 PHENYLACETALDEHYDE DIISOBUTYLACETAL(PADIB A) 6834-22-2 Phenylacetaldehyde dimethyl acetal 101-48-4 Phenylacetaldehyde glyceryl acetal 29895-73-6 Phenylacetic acid 103-82-2 Phenylalanine, DL-150-0-1 Phenylalanine, L-6-91-2 Phenylethyl 2-methylbutyrate 24817-51-4 Phenylethyl acetate 103-45-7 Phenylethyl anthranilate 133-18-6 PHENYLETHYL HEXANOATE 6290-37-5 Phenylethyl isoamyl ether 56011-02-0 Phenylethyl isovalerate 140-26-1 PHENYLETHYL ISOVALERATE 140-26-1 Phenylethyl methacrylate 3683-12-3 Phenylethyl methyl ether 3558-60-9 Phenylethyl methyl ethyl carbinol 10415-87-9 Phenylethyl methyl ethyl carbinol acetate 72007-81-9 Phenylethyl phenylacetate 102-20-5 PHENYLETHYL PIVALATE 67662-96-8 Phenylethyl propionate 122-70-3 Phenylethyl salicylate 87-22-9 Phenylethyl tiglate 55719-85-2 PHENYLETHYL TIGLATE 55719-85-2 Phenylpropionaldehyde, 2-93-53-8 Phenylpropionaldehyde, 3-104-53-0 Phenylpropionic acid, 3-501-52-0 Phenylpropyl acetate 122-72-5 Phenylpropyl butyrate, 2-80866-83-7 Phenylpropyl formate 104-64-3 Phenylpropyl isobutyrate, 2-65813-53-8 Phenylpropyl isobutyrate, 2-65813-53-8 Phenylpropyl isobutyrate, 3-103-58-2 Phenylpropyl isovalerate, 3-5452-07-3 Phenylpropyl propionate 122-74-7 Phenylpropyl)pyridine, 2-(3-2110-18-8 Phytol 150-86-7 Pimenta berry oil 8006-77-7 Pimenta leaf oil 8016-45-3 Pinacol 76-09-5 Pinacol 76-09-5 Pinanol, cis-2-4948-28-1 Pinene, (1S)-(1)-alpha-7785-26-4 Pinene, (1S)-(1)-beta-18172-67-3 Pinene, alpha-80-56-8 PlNENE, BETA-127-91-3 Pinus pumilio oil 8000-26-8 Pinus sylvestris oil 8023-99-2 Piperidine 110-89-4 Piperine 94-62-2 PlPERITENONE 89-81-6 Piperonal 120-57-0 Piperonyl acetate 326-61-4 Piperonyl acetone 55418-52-5 Piperonyl isobutyrate 5461-08-5 PRENOL 556-82-1 Prenyl acetate 1191-16-8 PRENYL BENZOATE 5205-11-8 Propanedithiol, 1,3-109-80-8 Propanol, 1-71-23-8 Propenoic acid, 3-phenyl-, 3-phenylpropyl ester, 2-122-68-9 Propionaldehyde 123-38-6 Propionic acid 79-09-4 Propiophenone 93-55-0 Propyl hexanoate 626-77-7 Propyl acetal, n-105-82-8 Propyl acetate 109-60-4 Propyl butyrate 105-66-8 Propyl disulfide 629-19-6 Propyl formate 110-74-7 Propyl heptanoate 7778-87-2 PROPYL HEXANOATE 626-77-7 Propyl isobutyrate 644-49-5 Propyl mercaptan 107-03-9 Propyl phenylacetate 4606-15-9 PROPYL PROPIONATE 106-36-5 Propylbicyclo[2.2.1]hept-5-ene-2-carboxaldehyde 39067-39-5 Propylene glycol 57-55-6 PROPYLIDENE PHTHALIDE 17369-59-4 Propylidenephthalide, 3-17369-59-4 Propyphenol, 2-644-5-9 Propyphenol, 4-645-56-7 Pulegone, (R)-(+)-89-82-7 PULEGONE, d-89-82-7 Pyrazineethanethiol 35250-5-4 Pyridine 110-86-1 Pyrrole 109-97-7 Pyrrolidine 123-75-1 Pyruvaldehyde 78-98-8 Pyruvic acid 127-17-3 Quinoline 91-22-5 RASPBERRY KETONE 5471-51-2 RASPBERRY KETONE METHYL ETHER 104-20-1 RESEDOLAN Phenylacetaldehyde hexylene 67633-94-7 Resorcinol 108-46-3 Rhodinol 6812-78-8 RHODINYL ACETATE 14 1-1 1-7 Rhodinyl butyrate 141-15-1 RHODINYL FORMATE 141-09-3 RHODINYL ISOBUTYRATE 138-23-8 Rhodinyl phenylacetate 10486-14- Rhodinyl propionate 105-89-5 Ribose, D-50-69-1 Rose absolute 8007-01-0 Rose oil 8007-01-0 ROSE OXIDE (High Cis) 16409-43-1 Rose oxide, levo 3033-23-6 Rosemary oil 8000-25-7 Rue oil 8014-29-7 Safranal 116-26-7 Safrole 94-59-7 Sage Dalmatian oil 8016-64-6 Sage oil 8016-65-7 SALICYLALDEHYDE 90-02-8 Sandlewood oil 8006-87-9 SANTALOL, ALPHA, BETA-115-71-9 Santalyl acetate 1323-00-8 Sassafras oil 8006-80-2 Savory summer oil 8016-68-0 Schinus molle oil 68917-52-2 Sclareol 515-03-7 Skatole 83-34-1 Snakeroot oil 8016-69-1 Sodium acetate 127-09-3 Sodium Benzoate 532-32-1 Sorbitan monostearate 1338-41-6 Sorbitol, D-50-70-4 Spearmint oil 8008-79-5 Spike lavender oil 8016-78-2 Spruce oil 8008-80-8 Star anise oil 8007-70- Stearic acid 57-11-4 Styralyl alcohol 98-85-1 Styrene 100-42-5 Sucrose octaacetate 126-14-7 Sweet birch oil 68917-50-0 Tagetes oil 8016-84-0 Tangelo oil 72869-73-9 Tangerine oil 8008-31-9 Tansy oil 8016-87-3 Tatatric acid, L-(+)-87-69-4 Tea tree oil 68647-73-4 TERPENYL FORMATE 2153-26-6 TERPINENE, ALPHA-99-86-5 TERPINENE, GAMMA-99-85-4 Terpineol 8000-41-7 Terpineol, alpha-10482-56-1 TERPINOLENE 586-62-9 TERPINYL ACETATE 8026-2 Terpinyl isobutyrate 7774-65-4 TERPINYL PROPIONATE 80-27-3 Terpnyl formate 2153-26-3 Tetradecalactone 2721-22-4 Tetrahydro-3,6-dimethyl-benzofuran, 4,5,6,7-494-90-6 Tetrahydro-4-methyl-2-(2-methylpropen-1-yl)pyran 16409-43-1 Tetrahydrofurfuryl acetate 637-64-9 Tetrahydrofurfuryl alcohol 97-99-4 Tetrahydrofurfuryl butyrate 92345-48-7 Tetrahydrogeranial 5988-91-0 Tetrahydrogeranyl acetate 20780-48-7 Tetrahydrolinalool 78-69-3 TETRAHYDROMUGUOL 41678-36-8 Tetrahydroquinoxaline, 5,6,7,8-34413-35-9 Tetramethyl-4-ethoxyvinylcyclohexanone, 3,3,5,5-36306-87-3 Tetramethypyrazine, 2,3,5,6-1124-11-4 Tetrohydrofurfuryl propionate, dl-637-65-0 Theaspirane 36431-72-8 Thiamine hydrochloride 67-03-8 Thiazole 288-47-1 Thienyl disulfide, 2-6911-51-9 Thiophenethiol 7774-74-5 Thyme oil 8007-46-3 Thymol 89-83-8 THYMYL ACETATE 528-79-0 THYMYL METHYL ETHER 1076-56-8 TIGLIC ACID 80-59-1 Tobacco leaf absolute 8037-19-2 Tolu, balsam, gum 9000-64-0 Tolualdehyde glyceryl acetal 1333-09-1 Toluenethiol, o-137-06-4 TOLUIC ALDEHYDE (MIXED 2,3,4) 1334-78-7 TOLYL ACETALDEHYDE, PARA-104-09-6 TOLYL ACETATE, PARA-140-39-6 Tolyl alcohol, p-589-18-4 Tolyl isobutyrate, o-36438-54-7 TOLYL PHENYL ACETATE, PARA-101-94-0 Tonka absolute 8046-22-8 Treemoss concrete 68648-41-9 TRIACETIN 102-76-1 Tributyl acetylcitrate 77-90-7 Tributyrin 60-01-5 Trichloromethyl phenyl carbinyl acetate 90-17-5 Tricyclodecen-4-yl 8-acetate 5413-60-5 Tricyclodecenyl propionate 17511-60-3 Tricyclodecylidene butanal 30168-23-1 Tridecanone, 2-59-08-8 TRIDECENAL, 2-7774-82-5 Triethyl citrate 77-93-0 Triethyl orthoformate 122-51-0 Triethyleneglycol 112-27-6 Trimethy-3,5,9-undecatrien-2-one, 3,6,10-1117-41-5 Trimethyl-1-cyclohexene-1-acetaldehyde, 2,6,6-472-66-2 Trimethyl-1-hexanol, 3,5,5-452-97-9 Trimethyl-2-cyclohexen)-2-methylbutanal, 4-(2,6,6-65405-84-7 Trimethyl-2-cyclohexen-1-yl)-3-buten-2-one, 4-(2,4,6-67801-38-1 Trimethyl-9-undecenal, 2,6,10-141-13-9 Trimethylamine 75-50-3 Trimethylbenzyl alcohol, p,alpha,alpha 1197-01-9 Trimethylcyclohexanol acetate, 3,5,5-58430-94-7 Trimethylcyclohexanol, 3,5,5-116-02-9 Trimethylcyclohexanone, 2,2,6-2408-7-9 Trimethylhexanal, 3,5,5-545-64- Trimethylpyrazine, 2,3,5-14667-55-1 Trimethylthiazole, 2,4,5-1623-11-5 Tripropionin 139-45-7 Trithioacetone 828-26-2 Tyrosine, L-60-18-4 Undecalactone, delta-710-04-3 Undecalactone, gamma-104-67-6 Undecanal 112-44-7 Undecane 1120-21-4 Undecanoic acid 112-37-8 Undecatriene, 1, 3, 5-16356-11-9 Undecenal, trans-2-53448-07-0 Undecylenic acid 112-38-9 Undecylenic aldehyde digeranyl acetal 67785-74-4 Valeraldehyde, n-110-62-3 Valeric acid 109-52-4 Valerolactone, gamma--108-29-2 Valine, dl-516-06-3 Valine, dl-516-06-3 Vanilla tincture 8047-24-3 VANILLIC ACID 121-34-6 Vanillin 121-33-5 VANILLIN ACETATE 881-68-5 Vanillin isobutyrate 20665-85-4 Vanillin propylene glycol acetal 68527-74-2 VANILLYL ACETONE 122-48-5 VANILLYL ALCOHOL 498-00-0 Vanillyl butyl ether 82654-98-6 Veratraldehyde 120-14-9 Veratrole 91-16-7 Verbena absolute 8024-12-2 VERBENONE, (1S)-(−)-1196-01-6 Vetiver acetate 117-98-6 Vetiver oil 8016-96-4 Vetiverol 89-88-3 Vinylphenol, 4-2628-17-3 Violet leaf absolute 9047-36-7 Whiskey lactone 39212-23-2 Xylenol, 2,4-105-67-9 Xylenol, 2,5-95-87-4 Xylenol, 2,6-576-26-1 Xylenol, 3,4-95-65-8 Xylose, d-58-86-6 Yarmor pine oil 8002-09-3 Ylang ylang oil 8006-81-3 Zingerone 122-48-5

TABLE A.2 Database of 138 odorant components CAS and CID Numbers (duplicate entries correspond to different odor intensity) CAS CID NAME 698-10-2 61199 Abhexone 98-86-2 7410 Acetophenone 1122-62-9 14286 ortho-Acetyl pyridine 141-13-9 98403 Adoxal 77-83-8 6501 Aldehyde c-16 (Lower)—glycidate 77-83-8 6501 Aldehyde c-16 (Higher)—glycidate 104-61-0 7710 gamma-nonalactone 123-68-2 31266 Allyl hexanoate 123-82-2 31276 amyl acetate: iso-amyl acetate 540-18-1 10890 amyl butyrate 60763-41-9 108505 amyl cinnamic aldehyde diethyl acetal 102-19-2 7600 iso-pentyl phenyl acetate 2173-56-0 62433 pentyl valerate 29597-36-2 and 122510 andrane 13567-39-0 104-46-1 637563 anethole 100-66-3 7519 anisole 89-43-0 98118 Auralva—Methyl N-(3 100-52-7 240 Benzaldehyde 119-84-8 660 benzo dihydro pyrone 5655-61-8 93009 bornyl acetate: iso-bornyl acetate 107-92-6 264 butanoic acid 71-38-3 263 butanol: 1-butanol 544-40-1 11002 butyl sulfide 67634-06-4 106734 butyl quinoline: iso-butyl quinoline 78-22-2 2537 camphor: dl-camphor 99-49-0 439570 carvone: i-carvone 87-44-5 and 5281515 caryophyllene 118-65-0 33704-61-9 92292 cashmeran 17369-59-4 6259976 celeriax 89-68-9 6982 chlorothymol 104-55-2 307 cinnamic aldehyde 141-27-5 638011 citral 5585-39-7 21768 citralva 91-64-5 323 coumarin 108-39-4 342 cresol: m-cresol 106-44-5 2879 p-Cresol 140-39-6 8797 cresyl acetate: p-cresyl acetate: p-tolyl ester 103-93-5 7685 cresyl butyrate: p-cresyl-iso-butyrate: P- tolyl ester 104-93-8 7731 cresyl methyl ether: 4-methyl anisole 122-03-2 326 cuminic aldehyde 1423-46-7 101150 cyclocitral: iso-cyclocitral 55704-78-4 62105 cyclodithalfarol 765-87-7 13006 cyclohexanedione 108-93-0 7966 cyclohexanol 80-71-7 6660 cyclotene 67634-23-5 236687 cyclotropal 25152-84-5 5283349 decadienal: 2 91-17-8 7044 decahydro naphthalene 111-92-2 8148 dibutyl amine 352-93-2 9609 diethyl sulfide 10094-34-5 24915 dimethyl benzyl carbinyl butyrate 103-05-9 7632 dimethyl phenyl ethyl carbinol 5910-89-4 22201 Dimethyl Pyrazine: 2 123-32-0 31252 Dimethyl Pyrazine: 2 625-84-3 12265 dimethyl pyrrole: 2 3658-80-8 19310 Dimethyl trisulfide 03-07-47 78484 diola 101-84-8 7583 Diphenyl Oxide 105-54-4 7762 ethyl butyrate 105-37-3 7749 ethyl propionate 13925-00-3 26331 2-ethyl pyrazine (low concentration) 13925-00-3 26331 2-ethyl pyrazine (higher concentration) 470-82-6 2758 eucalyptol 97-53-0 3314 eugenol 67634-15-5 105513 floralozone 01-10-13 80865 fructone 98-01-1 7362 furfural 98-02-2 7363 furfuryl mercaptan 88683-93-6 grisalva 90-05-1 460 Guaiacol 111-71-7 8130 Heptanal 111-70-6 8129 1-heptanol 66-25-1 6184 hexanal 142-62-1 8892 hexanoic acid 111-27-3 8103 1-hexanol 623-37-0 12178 3-Hexanol 6728-26-3 5281168 trans-1-Hexanal 111-26-2 8102 hexyl amine (lower concen) 111-26-2 8102 hexyl amine (higher concen) 101-86-0 1550884 hexyl cinnamic aldehyle 90-87-9 62336 Hydratropic Aldehyde Dimethyl Acetate 107-75-5 7888 hydroxy Citronellal 120-72-9 798 Indole 67801-36-9 106826 indolene 75-47-8 6374 iodoform 14901-07-6 638014 ionone: beta-ionone (lower con) 14901-07-6 638014 ionone: beta-ionone (higher con) 79-69-6 5371002 irone: alpha-Irone 126-91-0 443158 linalool 138-86-3 22311 limonene: d-Limonene 31906-04-4 91604 lyral 672558-87-1 maritima 106-72-9 61016 melonal 2216-51-5 16666 menthol: 1-Menthol methoxy-naphthalene: 2-Methoxy 93-04-9 7119 Naphthalene 134-20-3 8635 methyl anthranilate 4744-10-9 20859 methyl acetaldehyde dimethyl acetal 1334-76-5 14918 methyl furoate 2371-42-8 16913 methyl-iso-borneol: 2-methyl-iso-borneol 91-62-3 7059 methyl quinoline: para-methyl quinoline 08-09-59 227085 Methyl iso-nicotinate 119-36-8 4133 methyl salicylate 2432-51-1 62444 Methyl Thiobutyrate 1222-05-5 91497 musk galaxolide 1506-02-1 89440 musk tonalid 37677-14-8 93199 myracaldehyde 143-13-5 8918 nonyl acetate 4674-50-4 20797 nootkatone 111-87-5 957 1-octanol 3391-86-4 18827 octenol: 1-octen 3-ol 109-52-4 7991 pentanoic acid 591-80-0 61138 4-Pentenoic acid 103-82-2 999 Phenyl acetic acid 536-74-3 10821 phenyl acetylene 60-12-8 6054 phenyl Ethanol (lower con) 60-12-8 6054 phenyl Ethanol (higher con) 78-59-1 6544 phorone: iso-Phorone 80-56-8 6654 pinene: alpha-Pinene 105-66-8 7770 propyl butyrate 135-79-5 67285 propyl quinoline: iso-propyl quinoline 111-47-7 8118 Propyl sulfide 110-86-1 1049 pyridine 94-59-7 5144 safrole 69460-08-8 sandiff 115-71-9 5281531 santalol 83-34-1 6736 skatole 10482-56-1 17100 Terpineol 110-01-1 1127 tetrahydro thiophene 91-61-2 66678 tetraquinone 36267-71-7 61951 thienopyrimidine 123-93-3 31277 thioglycolic acid 110-02-1 8030 thiophene 89-83-8 6989 Thymol 529-20-4 10722 tolualdehyde: ortho-Tolualdehyde 108-88-3 1140 toluene (lower con) 108-88-3 1140 toluene (higher con) 75-50-3 1146 trimethyl amine 104-67-6 7714 undecalactone: gamma-Undecalactone 112-38-9 5634 undecylenic acid 590-86-3 11552 valeraldehyde: iso-valeraldehyde 503-74-2 10430 valeric acid: iso-valeric acid 108-29-2 7921 valerolactone: gamma-valerolactone 121-33-5 1183 Vanillin 122-48-5 31211 Zingerone

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Although the invention has been described in conjunction with specific embodiments thereof, it is evident that many alternatives, modifications and variations will be apparent to those skilled in the art. Accordingly, it is intended to embrace all such alternatives, modifications and variations that fall within the spirit and broad scope of the appended claims.

All publications, patents and patent applications mentioned in this specification are herein incorporated in their entirety by reference into the specification, to the same extent as if each individual publication, patent or patent application was specifically and individually indicated to be incorporated herein by reference. In addition, citation or identification of any reference in this application shall not be construed as an admission that such reference is available as prior art to the present invention. To the extent that section headings are used, they should not be construed as necessarily limiting. 

What is claimed is:
 1. A method of producing an odorant mixture to mask odors, the method comprising: for each odorant component from a first group of M odorant components, obtaining, by a data processor, a multidimensional vector of attributes characterizing the given odorant component, thereby providing an olfactory space of M multidimensional vectors corresponding to the M odorant components; selecting, by the data processor, a second group of N odorant components among the M odorant components of the first group, wherein a z-score indicative of a uniformity of distribution of the N odorant components from the second group over the olfactory space is less than 2, and wherein the z-score is calculated as an average of characteristic distances between vectors corresponding to odorant components in the second group and vectors corresponding to odorant components in the first group but not belonging to the second group; and mixing said N odorant components to form the odorant mixture.
 2. The method according to claim 1, wherein each characteristic distance is defined as a minimum distance between a vector corresponding to odorant components in the second group and a vector corresponding to an odorant component in the first group but not belonging to the second group.
 3. The method according to claim 1, wherein each of said multidimensional vectors has at least 50 dimensions.
 4. The method according to claim 1, wherein each of said multidimensional vectors has at least 100 dimensions.
 5. The method according to claim 1, wherein each of said multidimensional vectors has at least 1000 dimensions.
 6. The method according to claim 1, wherein N equals at least
 30. 7. The method according to claim 1, wherein N equals at least
 40. 8. The method according to claim 1, wherein N equals at least
 50. 9. The method according to claim 1, wherein M equals at least
 100. 10. The method according to claim 1, wherein M equals at least
 1000. 11. The method according to claim 1, wherein the first group of M odorant components is selected from the odorant components listed in Table A.1 or Table A.2 of Annex
 1. 12. The method according to claim 1, wherein the z-score is calculated with respect to a synthetic database which comprises a plurality of entries, each corresponding to a database odorant mixture defined as being producible from a plurality of odorant components selected from the first group of M odorant components.
 13. The method according to claim 12, further comprising generating said synthetic database.
 14. The method according to claim 1, wherein N equals at least
 20. 